diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 07a53170b7..071686d373 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -14,9 +14,11 @@ build:
   jobs:
     pre_create_environment:
       # update mamba just in case
-      - mamba update --yes --quiet --name=base mamba
+      - mamba update --yes --quiet --name=base mamba 'zstd=1.5.2'
       - mamba --version
+      - mamba list --name=base
     post_create_environment:
+      - conda run -n ${CONDA_DEFAULT_ENV} mamba list
       # use conda run executable wrapper to have all env variables
       - conda run -n ${CONDA_DEFAULT_ENV} mamba --version
       - conda run -n ${CONDA_DEFAULT_ENV} pip install . --no-deps
diff --git a/.zenodo.json b/.zenodo.json
index 083fa51356..43d9d2638a 100644
--- a/.zenodo.json
+++ b/.zenodo.json
@@ -86,6 +86,11 @@
             "name": "Bock, Lisa",
             "orcid": "0000-0001-7058-5938"
         },
+        {
+            "affiliation": "DLR, Germany",
+            "name": "Bonnet, Pauline",
+            "orcid": "0000-0003-3780-0784"
+        },
         {
             "affiliation": "BSC, Spain",
             "name": "Caron, Louis-Philippe",
@@ -176,6 +181,11 @@
             "name": "Hassler, Birgit",
             "orcid": "0000-0003-2724-709X"
         },
+        {
+            "affiliation": "DLR, Germany",
+            "name": "Heuer, Helge",
+            "orcid": "0000-0003-2411-7150"
+        },
         {
             "affiliation": "BSC, Spain",
             "name": "Hunter, Alasdair",
@@ -194,6 +204,10 @@
             "affiliation": "MPI for Biogeochemistry, Germany",
             "name": "Koirala, Sujan"
         },
+        {
+            "affiliation": "DLR, Germany",
+            "name": "Kuehbacher, Birgit"
+        },
         {
             "affiliation": "BSC, Spain",
             "name": "Lledó, Llorenç"
@@ -263,6 +277,10 @@
             "affiliation": "CICERO, Norway",
             "name": "Sandstad, Marit"
         },
+        {
+            "affiliation": "DLR, Germany",
+            "name": "Sarauer, Ellen"
+        },
         {
             "affiliation": "MetOffice, UK",
             "name": "Sellar, Alistair"
@@ -338,6 +356,15 @@
             "affiliation": "DLR, Germany",
             "name": "Kazeroni, Rémi",
             "orcid": "0000-0001-7205-9528"
+        },
+        {
+            "affiliation": "DLR, Germany",
+            "name": "Kraft, Jeremy"
+        },
+        {
+            "affiliation": "University of Bremen, Germany",
+            "name": "Ruhe, Lukas",
+            "orcid": "0000-0001-6349-9118"
         }
     ],
     "description": "ESMValTool: A community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP.",
diff --git a/CITATION.cff b/CITATION.cff
index 49226a7fda..b6f6d45f77 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -181,6 +181,11 @@ authors:
     family-names: Hassler
     given-names: Birgit
     orcid: "https://orcid.org/0000-0003-2724-709X"
+  -
+    affiliation: "DLR, Germany"
+    family-names: Heuer
+    given-names: Helge
+    orcid: "https://orcid.org/0000-0003-2411-7150"
   -
     affiliation: "BSC, Spain"
     family-names: Hunter
@@ -199,6 +204,10 @@ authors:
     affiliation: "MPI for Biogeochemistry, Germany"
     family-names: Koirala
     given-names: Sujan
+  -
+    affiliation: "DLR, Germany"
+    family-names: Kuehbacher
+    given-names: Birgit
   -
     affiliation: "BSC, Spain"
     family-names: Lledó
@@ -300,6 +309,10 @@ authors:
     family-names: Weigel
     given-names: Katja
     orcid: "https://orcid.org/0000-0001-6133-7801"
+  -
+    affiliation: "DLR, Germany"
+    family-names: Sarauer
+    given-names: Ellen
   -
     affiliation: "University of Reading, UK"
     family-names: Roberts
@@ -354,6 +367,20 @@ authors:
     family-names: Beucher
     given-names: Romain
     orcid: "https://orcid.org/0000-0003-3891-5444"
+  -
+    affiliation: "DLR, Germany"
+    family-names: Kraft
+    given-names: Jeremy
+  -
+    affiliation: "University of Bremen, Germany"
+    family-names: Ruhe
+    given-names: Lukas
+    orcid: "https://orcid.org/0000-0001-6349-9118"
+  -
+    affiliation: "DLR, Germany"
+    family-names: Bonnet
+    given-names: Pauline
+    orcid: "https://orcid.org/0000-0003-3780-0784"
 
 cff-version: 1.2.0
 date-released: 2023-07-06
diff --git a/conda-linux-64.lock b/conda-linux-64.lock
index c7e91c6a20..128ace6209 100644
--- a/conda-linux-64.lock
+++ b/conda-linux-64.lock
@@ -13,35 +13,32 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-hab24e00_0.tar.bz2#19410c3df09dfb12d1206132a1d357c5
 https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-2.6.32-he073ed8_16.conda#7ca122655873935e02c91279c5b03c8c
 https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h41732ed_0.conda#7aca3059a1729aa76c597603f10b0dd3
-https://conda.anaconda.org/conda-forge/linux-64/libgcc-devel_linux-64-13.1.0-he3cc6c4_0.conda#5ec50dcd74ba7461709c4ac9c4cc4190
-https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.1.0-h15d22d2_0.conda#afb656a334c409dd9805508af1c89c7a
-https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-devel_linux-64-13.1.0-he3cc6c4_0.conda#e703914ad2288ab24cf5ac94d812fc11
-https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.1.0-hfd8a6a1_0.conda#067bcc23164642f4c226da631f2a2e1d
+https://conda.anaconda.org/conda-forge/linux-64/libgcc-devel_linux-64-13.2.0-ha9c7c90_2.conda#401c5cf212e568dab47e0677a000d2a7
+https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-devel_linux-64-13.2.0-ha9c7c90_2.conda#a0bf59008336aa76ad2a097a652c60bd
+https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h7e041cc_2.conda#9172c297304f2a20134fc56c97fbe229
 https://conda.anaconda.org/conda-forge/noarch/poppler-data-0.4.12-hd8ed1ab_0.conda#d8d7293c5b37f39b2ac32940621c6592
-https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-3_cp311.conda#c2e2630ddb68cf52eec74dc7dfab20b5
+https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-4_cp311.conda#d786502c97404c94d7d58d258a445a65
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-https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-13.1.0-h69a702a_0.conda#506dc07710dd5b0ba63cbf134897fc10
-https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.1.0-he5830b7_0.conda#56ca14d57ac29a75d23a39eb3ee0ddeb
+https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.2.0-h807b86a_2.conda#e2042154faafe61969556f28bade94b9
 https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.12-he073ed8_16.conda#071ea8dceff4d30ac511f4a2f8437cd1
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-https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.1.0-he5830b7_0.conda#cd93f779ff018dd85c7544c015c9db3c
+https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h807b86a_2.conda#c28003b0be0494f9a7664389146716ff
 https://conda.anaconda.org/conda-forge/linux-64/aom-3.5.0-h27087fc_0.tar.bz2#a08150fd2298460cd1fcccf626305642
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 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h7f98852_4.tar.bz2#a1fd65c7ccbf10880423d82bca54eb54
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 https://conda.anaconda.org/conda-forge/linux-64/charls-2.4.2-h59595ed_0.conda#4336bd67920dd504cd8c6761d6a99645
 https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda#418c6ca5929a611cbd69204907a83995
-https://conda.anaconda.org/conda-forge/linux-64/fftw-3.3.10-nompi_hc118613_108.conda#6fa90698000b05dfe8ce6515794fe71a
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 https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.10-h36c2ea0_0.tar.bz2#ac7bc6a654f8f41b352b38f4051135f8
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 https://conda.anaconda.org/conda-forge/linux-64/gettext-0.21.1-h27087fc_0.tar.bz2#14947d8770185e5153fdd04d4673ed37
 https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-he1b5a44_1004.tar.bz2#cddaf2c63ea4a5901cf09524c490ecdc
-https://conda.anaconda.org/conda-forge/linux-64/ghostscript-9.54.0-h27087fc_2.tar.bz2#c3b35ac18d09ffc8d46064fb09a696af
+https://conda.anaconda.org/conda-forge/linux-64/ghostscript-10.02.0-h59595ed_0.conda#8ab9369b573e62ddf9456f1fe4dfc39d
 https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.1-h0b41bf4_3.conda#96f3b11872ef6fad973eac856cd2624f
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 https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h58526e2_1001.tar.bz2#8c54672728e8ec6aa6db90cf2806d220
@@ -59,13 +56,12 @@ https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.18-h0b41bf4_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-h516909a_1.tar.bz2#6f8720dff19e17ce5d48cfe7f3d2f0a3
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 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3
+https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-ha4646dd_2.conda#78fdab09d9138851dde2b5fe2a11019e
 https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-h166bdaf_0.tar.bz2#b62b52da46c39ee2bc3c162ac7f1804d
-https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-2.1.5.1-h0b41bf4_0.conda#1edd9e67bdb90d78cea97733ff6b54e6
+https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-2.1.5.1-hd590300_1.conda#323e90742f0f48fc22bea908735f55e6
 https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.0-h7f98852_0.tar.bz2#39b1328babf85c7c3a61636d9cd50206
 https://conda.anaconda.org/conda-forge/linux-64/libnuma-2.0.16-h0b41bf4_1.conda#28bfe2cb11357ccc5be21101a6b7ce86
-https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.23-pthreads_h80387f5_0.conda#9c5ea51ccb8ffae7d06c645869d24ce6
-https://conda.anaconda.org/conda-forge/linux-64/libopenblas-ilp64-0.3.23-pthreads_h5c82d6a_0.conda#14e3fb938a49ea46df201eb105bb9068
-https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-13.1.0-hfd8a6a1_0.conda#7594fd17fb4d1b8b0e47a6b306fe01ae
+https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-13.2.0-h7e041cc_2.conda#aa27066e2dcef56db7bc674f2230b6c9
 https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.18-h36c2ea0_1.tar.bz2#c3788462a6fbddafdb413a9f9053e58d
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@@ -78,19 +74,18 @@ https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda#318
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-https://conda.anaconda.org/conda-forge/linux-64/metis-5.1.1-h59595ed_0.conda#9c9e667050d3e258d321e63a0989ebb1
-https://conda.anaconda.org/conda-forge/linux-64/nccl-2.18.3.1-h12f7317_0.conda#c0041b39fc72c1d12139b45363c8a510
+https://conda.anaconda.org/conda-forge/linux-64/metis-5.1.0-h59595ed_1007.conda#40ccb8318df2500f83bd868dd8fcd201
+https://conda.anaconda.org/conda-forge/linux-64/nccl-2.18.5.1-h6103f9b_1.conda#45a5f1a3859ca6547de507330afcf31a
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-https://conda.anaconda.org/conda-forge/linux-64/openssl-3.1.2-hd590300_0.conda#e5ac5227582d6c83ccf247288c0eb095
+https://conda.anaconda.org/conda-forge/linux-64/openssl-3.1.3-hd590300_0.conda#7bb88ce04c8deb9f7d763ae04a1da72f
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-https://conda.anaconda.org/conda-forge/linux-64/rocm-smi-5.6.0-h59595ed_1.conda#2980fb3b7777f495dacd46d5cc5d74bb
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@@ -103,7 +98,7 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-makedepend-1.0.8-h59595ed_0
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-https://conda.anaconda.org/conda-forge/linux-64/xxhash-0.8.1-h0b41bf4_0.conda#e9c3bcf0e0c719431abec8ca447eee27
+https://conda.anaconda.org/conda-forge/linux-64/xxhash-0.8.2-hd590300_0.conda#f08fb5c89edfc4aadee1c81d4cfb1fa1
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@@ -113,73 +108,71 @@ https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.2.17-h862ab7
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+https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-13.2.0-h338b0a0_2.conda#b4f2ab4faf22658cca303570fd9a7662
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+https://conda.anaconda.org/conda-forge/noarch/seaborn-0.12.2-hd8ed1ab_0.conda#50847a47c07812f88581081c620f5160
+https://conda.anaconda.org/conda-forge/noarch/xesmf-0.8.2-pyhd8ed1ab_0.conda#8e765a0eca0ce1cfa889cd9af82a23a8
+https://conda.anaconda.org/conda-forge/linux-64/xgboost-1.7.6-cuda112py311h2344cce_2.conda#97cd4e1589b8ea11e0f78d2f0c7cc217
+https://conda.anaconda.org/conda-forge/noarch/dask-2023.9.2-pyhd8ed1ab_0.conda#29e33df59c9eac1a599b9cd18d54b4d3
+https://conda.anaconda.org/conda-forge/noarch/nbconvert-pandoc-7.8.0-pyhd8ed1ab_0.conda#1dba1a577df2625a24667612a069e91c
 https://conda.anaconda.org/conda-forge/noarch/prov-2.0.0-pyhd3deb0d_0.tar.bz2#aa9b3ad140f6c0668c646f32e20ccf82
+https://conda.anaconda.org/conda-forge/noarch/psy-maps-1.4.2-pyhd8ed1ab_0.tar.bz2#3ed13103dfd46f71dc870d188bd0b276
 https://conda.anaconda.org/conda-forge/linux-64/psy-reg-1.4.0-py311h38be061_3.conda#6f7871722c07922028043144e8873b37
 https://conda.anaconda.org/conda-forge/noarch/python-cdo-1.6.0-pyhd8ed1ab_0.conda#3fd1a0b063c1fbbe4b7bd5a5a7601e84
 https://conda.anaconda.org/conda-forge/linux-64/r-akima-0.6_2.3-r41h92ddd45_0.tar.bz2#bac0b7627ef744c98f4bc48885f52e72
@@ -619,10 +620,8 @@ https://conda.anaconda.org/conda-forge/noarch/r-scales-1.2.1-r41hc72bb7e_1.tar.b
 https://conda.anaconda.org/conda-forge/linux-64/r-specsverification-0.5_3-r41ha503ecb_3.conda#2bc51f0d44b98092ba57cf2f8671b490
 https://conda.anaconda.org/conda-forge/linux-64/r-splancs-2.01_43-r41h8da6f51_1.tar.bz2#3a6aad0706541141d10e3b514467a080
 https://conda.anaconda.org/conda-forge/linux-64/r-vctrs-0.6.2-r41ha503ecb_0.conda#1f7610a1863648cab254a9f85bd29dcd
-https://conda.anaconda.org/conda-forge/noarch/seaborn-0.12.2-hd8ed1ab_0.conda#50847a47c07812f88581081c620f5160
-https://conda.anaconda.org/conda-forge/linux-64/xgboost-1.7.6-cuda112py311h2344cce_0.conda#bfa2be4c1df9bf875896b40b1735e6c2
-https://conda.anaconda.org/conda-forge/noarch/iris-esmf-regrid-0.7.0-pyhd8ed1ab_0.conda#de82eb8d09362babacafe6b7e27752ac
-https://conda.anaconda.org/conda-forge/noarch/nbconvert-7.7.3-pyhd8ed1ab_0.conda#f53d92ecd7d8563b006107f6a33e55c6
+https://conda.anaconda.org/conda-forge/noarch/iris-esmf-regrid-0.8.0-pyhd8ed1ab_0.conda#56e85460d22fa7d4fb06300f785dd1e1
+https://conda.anaconda.org/conda-forge/noarch/nbconvert-7.8.0-pyhd8ed1ab_0.conda#43bce95e8c474dd21d7ed5de8b4806f7
 https://conda.anaconda.org/conda-forge/noarch/r-cyclocomp-1.1.0-r41hc72bb7e_1005.tar.bz2#800e1da5bf774be48934b8865dd78d33
 https://conda.anaconda.org/conda-forge/noarch/r-gridextra-2.3-r41hc72bb7e_1004.tar.bz2#71ebed7e976df735ff3443bb88bd154f
 https://conda.anaconda.org/conda-forge/noarch/r-httr-1.4.6-r41hc72bb7e_0.conda#53dbb769c96782db54bf2d414fc9b239
@@ -648,11 +647,11 @@ https://conda.anaconda.org/conda-forge/linux-64/r-geomap-2.5_0-r41h06615bd_1.tar
 https://conda.anaconda.org/conda-forge/noarch/r-lintr-3.0.2-r41hc72bb7e_0.tar.bz2#769c2305486b74fd61d85bfef2296f27
 https://conda.anaconda.org/conda-forge/noarch/r-s2dverification-2.10.3-r41hc72bb7e_1.tar.bz2#2253f130c8dab435824d6ddb10a41c73
 https://conda.anaconda.org/conda-forge/noarch/autodocsumm-0.2.6-pyhd8ed1ab_0.tar.bz2#4409dd7e06a62c3b2aa9e96782c49c6d
-https://conda.anaconda.org/conda-forge/noarch/nbsphinx-0.9.2-pyhd8ed1ab_0.conda#d1212b423fdd10d2da59601385561ff7
-https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.13.3-pyhd8ed1ab_0.conda#07aca5f2dea315dcc16680d6891e9056
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-1.0.6-pyhd8ed1ab_0.conda#5bba7b5823474cb3fcd4e4cbf942da61
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.4-pyhd8ed1ab_0.conda#73dcd0eb2252cbd1530fd1e6e3cbbb03
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.3-pyhd8ed1ab_0.conda#fb4d6329a57e20e03d7aecd18c7ca918
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-1.0.5-pyhd8ed1ab_0.conda#85466265b76473cc1d02420056cbc4e3
-https://conda.anaconda.org/conda-forge/noarch/sphinx-7.1.2-pyhd8ed1ab_0.conda#d02bfa35cd4f2cd624289f64911cae9d
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.7-pyhd8ed1ab_0.conda#01e35beea8aff61cdb445b90a7adf7d4
+https://conda.anaconda.org/conda-forge/noarch/nbsphinx-0.9.3-pyhd8ed1ab_0.conda#0dbaa7d08d3d79b2a1a4dd6a02cc4581
+https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.14.1-pyhd8ed1ab_0.conda#78153addf629c51fab775ef360012ca3
+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-1.0.7-pyhd8ed1ab_0.conda#aebfabcb60c33a89c1f9290cab49bc93
+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.5-pyhd8ed1ab_0.conda#ebf08f5184d8eaa486697bc060031953
+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.4-pyhd8ed1ab_0.conda#a9a89000dfd19656ad004b937eeb6828
+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-1.0.6-pyhd8ed1ab_0.conda#cf5c9649272c677a964a7313279e3a9b
+https://conda.anaconda.org/conda-forge/noarch/sphinx-7.2.6-pyhd8ed1ab_0.conda#bbfd1120d1824d2d073bc65935f0e4c0
+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.9-pyhd8ed1ab_0.conda#0612e497d7860728f2cda421ea2aec09
diff --git a/doc/sphinx/source/recipes/figures/monitor/hovmoeller_time_vs_lat_with_ref.png b/doc/sphinx/source/recipes/figures/monitor/hovmoeller_time_vs_lat_with_ref.png
new file mode 100644
index 0000000000..4abd6df04f
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/monitor/hovmoeller_time_vs_lat_with_ref.png differ
diff --git a/doc/sphinx/source/recipes/figures/monitor/hovmoeller_z_vs_time_with_ref.png b/doc/sphinx/source/recipes/figures/monitor/hovmoeller_z_vs_time_with_ref.png
new file mode 100755
index 0000000000..734913c60b
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/monitor/hovmoeller_z_vs_time_with_ref.png differ
diff --git a/doc/sphinx/source/recipes/figures/monitor/variable_vs_lat_with_ref.png b/doc/sphinx/source/recipes/figures/monitor/variable_vs_lat_with_ref.png
new file mode 100644
index 0000000000..31cb81135d
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/monitor/variable_vs_lat_with_ref.png differ
diff --git a/doc/sphinx/source/recipes/recipe_monitor.rst b/doc/sphinx/source/recipes/recipe_monitor.rst
index 0358ec36a7..9bdfd5d40b 100644
--- a/doc/sphinx/source/recipes/recipe_monitor.rst
+++ b/doc/sphinx/source/recipes/recipe_monitor.rst
@@ -210,3 +210,24 @@ Zonal mean profile of ta including a reference dataset.
    :width:   14cm
 
 1D profile of ta including a reference dataset.
+
+.. _fig_variable_vs_lat_with_ref:
+.. figure::  /recipes/figures/monitor/variable_vs_lat_with_ref.png
+   :align:   center
+   :width:   14cm
+
+Zonal mean pr including a reference dataset.
+
+.. _fig_hovmoeller_z_vs_time_with_ref:
+.. figure::  /recipes/figures/monitor/hovmoeller_z_vs_time_with_ref.png
+   :align:   center
+   :width:   14cm
+
+Hovmoeller plot (pressure vs. time) of ta including a reference dataset.
+
+.. _fig_hovmoeller_time_vs_lat_with_ref:
+.. figure:: /recipes/figures/monitor/hovmoeller_time_vs_lat_with_ref.png
+   :align:   center
+   :width:   14cm
+
+Hovmoeller plot (time vs. latitude) of tas including a reference dataset
diff --git a/esmvaltool/config-references.yml b/esmvaltool/config-references.yml
index baaaa44c96..cea1a0561b 100644
--- a/esmvaltool/config-references.yml
+++ b/esmvaltool/config-references.yml
@@ -124,6 +124,11 @@ authors:
     name: Bojovic, Dragana
     institute: BSC, Spain
     orcid: https://orcid.org/0000-0001-7354-1885
+  bonnet_pauline:
+    name: Bonnet, Pauline
+    institute: DLR, Germany
+    orcid: https://orcid.org/0000-0003-3780-0784
+    github: Paulinebonnet111
   brunner_lukas:
     name: Brunner, Lukas
     institute: ETH Zurich, Switzerland
@@ -252,6 +257,11 @@ authors:
     name: Hempelmann, Nils
     institute: IPSL, France
     orcid:
+  heuer_helge:
+    name: Heuer, Helge
+    institute: DLR, Germany
+    email: helge.heuer@dlr.de
+    orcid: https://orcid.org/0000-0003-2411-7150
   hogan_emma:
     name: Hogan, Emma
     institute: MetOffice, UK
@@ -288,10 +298,20 @@ authors:
     name: Koirala, Sujan
     institute: MPI-BGC, Germany
     orcid: https://orcid.org/0000-0001-5681-1986
+  kraft_jeremy:
+    name: Kraft, Jeremy
+    institute: DLR, Germany
+    orcid:
+    github: jeremykraftdlr
   krasting_john:
     name: Krasting, John
     institute: NOAA, USA
     orcid: https://orcid.org/0000-0002-4650-9844
+  kuehbacher_birgit:
+    name: Kuehbacher, Birgit
+    institute: DLR, Germany
+    email: birgit.kuehbacher@dlr.de
+    orcid:
   lejeune_quentin:
     name: Lejeune, Quentin
     institute: Climate Analytics, Germany
@@ -446,6 +466,11 @@ authors:
     name: Sandstad, Marit
     institute: Cicero, Norway
     orcid:
+  sarauer_ellen:
+    name: Sarauer, Ellen
+    institute: DLR, Germany
+    orcid:
+    github: ellensarauer
   serva_federico:
     name: Serva, Federico
     institute: CNR, Italy
diff --git a/esmvaltool/diag_scripts/monitor/multi_datasets.py b/esmvaltool/diag_scripts/monitor/multi_datasets.py
index 6ac399652b..4299e2538c 100644
--- a/esmvaltool/diag_scripts/monitor/multi_datasets.py
+++ b/esmvaltool/diag_scripts/monitor/multi_datasets.py
@@ -31,7 +31,7 @@
       datasets need to be given on the same horizontal and vertical grid (you
       can use the preprocessors :func:`esmvalcore.preprocessor.regrid` and
       :func:`esmvalcore.preprocessor.extract_levels` for this). Input data
-      needs to be 2D with dimensions `latitude`, `height`/`air_pressure`.
+      needs to be 2D with dimensions `latitude`, `altitude`/`air_pressure`.
 
       .. warning::
 
@@ -42,7 +42,28 @@
 
     - 1D profiles (plot type ``1d_profile``): for each variable separately, all
       datasets are plotted in one single figure. Input data needs to be 1D with
-      single dimension `height` / `air_pressure`
+      single dimension `altitude` / `air_pressure`
+    - Variable vs. latitude plot (plot type ``variable_vs_lat``):
+      for each variable separately, all datasets are plotted in one
+      single figure. Input data needs to be 1D with single
+      dimension `latitude`.
+    - Hovmoeller Z vs. time (plot type ``hovmoeller_z_vs_time``): for each
+      variable and dataset, an individual figure is plotted. If a reference
+      dataset is defined, also include this dataset and a bias plot into the
+      figure. Note that if a reference dataset is defined, all input datasets
+      need to be given on the same temporal and vertical grid (you can use
+      the preprocessors :func:`esmvalcore.preprocessor.regrid_time` and
+      :func:`esmvalcore.preprocessor.extract_levels` for this). Input data
+      needs to be 2D with dimensions `time`, `altitude`/`air_pressure`.
+    - Hovmoeller time vs. latitude or longitude (plot type
+      ``hovmoeller_time_vs_lat_or_lon``): for each variable and dataset, an
+      individual figure is plotted. If a reference dataset is defined, also
+      include this dataset and a bias plot into the figure. Note that if a
+      reference dataset is defined, all input datasets need to be given on the
+      same temporal and horizontal grid (you can use the preprocessors
+      :func:`esmvalcore.preprocessor.regrid_time` and
+      :func:`esmvalcore.preprocessor.regrid` for this). Input data
+      needs to be 2D with dimensions `time`, `latitude`/`longitude`.
 
 Author
 ------
@@ -60,12 +81,16 @@
 figure_kwargs: dict, optional
     Optional keyword arguments for :func:`matplotlib.pyplot.figure`. By
     default, uses ``constrained_layout: true``.
+group_variables_by: str, optional (default: 'short_name')
+    Facet which is used to create variable groups. For each variable group, an
+    individual plot is created.
 plots: dict, optional
     Plot types plotted by this diagnostic (see list above). Dictionary keys
-    must be ``timeseries``, ``annual_cycle``, ``map``, ``zonal_mean_profile``
-    or ``1d_profile``.
-    Dictionary values are dictionaries used as options for the corresponding
-    plot. The allowed options for the different plot types are given below.
+    must be ``timeseries``, ``annual_cycle``, ``map``, ``zonal_mean_profile``,
+    ``1d_profile``, ``variable_vs_lat``, ``hovmoeller_z_vs_time``,
+    ``hovmoeller_time_vs_lat_or_lon``. Dictionary values are dictionaries used
+    as options for the corresponding plot. The allowed options for the
+    different plot types are given below.
 plot_filename: str, optional
     Filename pattern for the plots.
     Defaults to ``{plot_type}_{real_name}_{dataset}_{mip}_{exp}_{ensemble}``.
@@ -119,6 +144,10 @@
     ``{project}`` that vary between the different datasets will be transformed
     to something like  ``ambiguous_project``. Examples: ``title: 'Awesome Plot
     of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+time_format: str, optional (default: None)
+    :func:`~datetime.datetime.strftime` format string that is used to format
+    the time axis using :class:`matplotlib.dates.DateFormatter`. If ``None``,
+    use the default formatting imposed by the iris plotting function.
 
 Configuration options for plot type ``annual_cycle``
 ----------------------------------------------------
@@ -270,7 +299,7 @@
 plot_func: str, optional (default: 'contourf')
     Plot function used to plot the profiles. Must be a function of
     :mod:`iris.plot` that supports plotting of 2D cubes with coordinates
-    latitude and height/air_pressure.
+    latitude and altitude/air_pressure.
 plot_kwargs: dict, optional
     Optional keyword arguments for the plot function defined by ``plot_func``.
     Dictionary keys are elements identified by ``facet_used_for_labels`` or
@@ -352,6 +381,189 @@
 show_y_minor_ticklabels: bool, optional (default: False)
     Show tick labels for the minor ticks on the Y axis.
 
+Configuration options for plot type ``variable_vs_lat``
+-------------------------------------------------------
+gridline_kwargs: dict, optional
+    Optional keyword arguments for grid lines. By default, ``color: lightgrey,
+    alpha: 0.5`` are used. Use ``gridline_kwargs: false`` to not show grid
+    lines.
+legend_kwargs: dict, optional
+    Optional keyword arguments for :func:`matplotlib.pyplot.legend`. Use
+    ``legend_kwargs: false`` to not show legends.
+plot_kwargs: dict, optional
+    Optional keyword arguments for :func:`iris.plot.plot`. Dictionary keys are
+    elements identified by ``facet_used_for_labels`` or ``default``, e.g.,
+    ``CMIP6`` if ``facet_used_for_labels: project`` or ``historical`` if
+    ``facet_used_for_labels: exp``. Dictionary values are dictionaries used as
+    keyword arguments for :func:`iris.plot.plot`. String arguments can include
+    facets in curly brackets which will be derived from the corresponding
+    dataset, e.g., ``{project}``, ``{short_name}``, ``{exp}``. Examples:
+    ``default: {linestyle: '-', label: '{project}'}, CMIP6: {color: red,
+    linestyle: '--'}, OBS: {color: black}``.
+pyplot_kwargs: dict, optional
+    Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
+    are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
+    single argument for these functions. String arguments can include facets in
+    curly brackets which will be derived from the datasets plotted in the
+    corresponding plot, e.g., ``{short_name}``, ``{exp}``. Facets like
+    ``{project}`` that vary between the different datasets will be transformed
+    to something like  ``ambiguous_project``. Examples: ``title: 'Awesome Plot
+    of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+
+Configuration options for plot type ``hovmoeller_z_vs_time``
+------------------------------------------------------------
+cbar_label: str, optional (default: '{short_name} [{units}]')
+    Colorbar label. Can include facets in curly brackets which will be derived
+    from the corresponding dataset, e.g., ``{project}``, ``{short_name}``,
+    ``{exp}``.
+cbar_label_bias: str, optional (default: 'Δ{short_name} [{units}]')
+    Colorbar label for plotting biases. Can include facets in curly brackets
+    which will be derived from the corresponding dataset, e.g., ``{project}``,
+    ``{short_name}``, ``{exp}``. This option has no effect if no reference
+    dataset is given.
+cbar_kwargs: dict, optional
+    Optional keyword arguments for :func:`matplotlib.pyplot.colorbar`. By
+    default, uses ``orientation: vertical``.
+cbar_kwargs_bias: dict, optional
+    Optional keyword arguments for :func:`matplotlib.pyplot.colorbar` for
+    plotting biases. These keyword arguments update (and potentially overwrite)
+    the ``cbar_kwargs`` for the bias plot. This option has no effect if no
+    reference dataset is given.
+common_cbar: bool, optional (default: False)
+    Use a common colorbar for the top panels (i.e., plots of the dataset and
+    the corresponding reference dataset) when using a reference dataset. If
+    neither ``vmin`` and ``vmix`` nor ``levels`` is given in ``plot_kwargs``,
+    the colorbar bounds are inferred from the dataset in the top left panel,
+    which might lead to an inappropriate colorbar for the reference dataset
+    (top right panel). Thus, the use of the ``plot_kwargs`` ``vmin`` and
+    ``vmax`` or ``levels`` is highly recommend when using this ``common_cbar:
+    true``. This option has no effect if no reference dataset is given.
+fontsize: int, optional (default: 10)
+    Fontsize used for ticks, labels and titles. For the latter, use the given
+    fontsize plus 2. Does not affect suptitles.
+log_y: bool, optional (default: True)
+    Use logarithmic Y-axis.
+plot_func: str, optional (default: 'contourf')
+    Plot function used to plot the profiles. Must be a function of
+    :mod:`iris.plot` that supports plotting of 2D cubes with coordinates
+    latitude and altitude/air_pressure.
+plot_kwargs: dict, optional
+    Optional keyword arguments for the plot function defined by ``plot_func``.
+    Dictionary keys are elements identified by ``facet_used_for_labels`` or
+    ``default``, e.g., ``CMIP6`` if ``facet_used_for_labels: project`` or
+    ``historical`` if ``facet_used_for_labels: exp``. Dictionary values are
+    dictionaries used as keyword arguments for the plot function defined by
+    ``plot_func``. String arguments can include facets in curly brackets which
+    will be derived from the corresponding dataset, e.g., ``{project}``,
+    ``{short_name}``, ``{exp}``. Examples: ``default: {levels: 2}, CMIP6:
+    {vmin: 200, vmax: 250}``.
+plot_kwargs_bias: dict, optional
+    Optional keyword arguments for the plot function defined by ``plot_func``
+    for plotting biases. These keyword arguments update (and potentially
+    overwrite) the ``plot_kwargs`` for the bias plot. This option has no effect
+    if no reference dataset is given. See option ``plot_kwargs`` for more
+    details. By default, uses ``cmap: bwr``.
+pyplot_kwargs: dict, optional
+    Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
+    are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
+    single argument for these functions. String arguments can include facets in
+    curly brackets which will be derived from the corresponding dataset, e.g.,
+    ``{project}``, ``{short_name}``, ``{exp}``.  Examples: ``title: 'Awesome
+    Plot of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+rasterize: bool, optional (default: True)
+    If ``True``, use `rasterization
+    <https://matplotlib.org/stable/gallery/misc/rasterization_demo.html>`_ for
+    profile plots to produce smaller files. This is only relevant for vector
+    graphics (e.g., ``output_file_type=pdf,svg,ps``).
+show_stats: bool, optional (default: True)
+    Show basic statistics on the plots.
+show_y_minor_ticklabels: bool, optional (default: False)
+    Show tick labels for the minor ticks on the Y axis.
+x_pos_stats_avg: float, optional (default: 0.01)
+    Text x-position of average (shown on the left) in Axes coordinates. Can be
+    adjusted to avoid overlap with the figure. Only relevant if ``show_stats:
+    true``.
+x_pos_stats_bias: float, optional (default: 0.7)
+    Text x-position of bias statistics (shown on the right) in Axes
+    coordinates. Can be adjusted to avoid overlap with the figure. Only
+    relevant if ``show_stats: true``.
+time_format: str, optional (default: None)
+    :func:`~datetime.datetime.strftime` format string that is used to format
+    the time axis using :class:`matplotlib.dates.DateFormatter`. If ``None``,
+    use the default formatting imposed by the iris plotting function.
+
+Configuration options for plot type ``hovmoeller_time_vs_lat_or_lon``
+---------------------------------------------------------------------
+cbar_label: str, optional (default: '{short_name} [{units}]')
+    Colorbar label. Can include facets in curly brackets which will be derived
+    from the corresponding dataset, e.g., ``{project}``, ``{short_name}``,
+    ``{exp}``.
+cbar_label_bias: str, optional (default: 'Δ{short_name} [{units}]')
+    Colorbar label for plotting biases. Can include facets in curly brackets
+    which will be derived from the corresponding dataset, e.g., ``{project}``,
+    ``{short_name}``, ``{exp}``. This option has no effect if no reference
+    dataset is given.
+cbar_kwargs: dict, optional
+    Optional keyword arguments for :func:`matplotlib.pyplot.colorbar`. By
+    default, uses ``orientation: vertical``.
+cbar_kwargs_bias: dict, optional
+    Optional keyword arguments for :func:`matplotlib.pyplot.colorbar` for
+    plotting biases. These keyword arguments update (and potentially overwrite)
+    the ``cbar_kwargs`` for the bias plot. This option has no effect if no
+    reference dataset is given.
+common_cbar: bool, optional (default: False)
+    Use a common colorbar for the top panels (i.e., plots of the dataset and
+    the corresponding reference dataset) when using a reference dataset. If
+    neither ``vmin`` and ``vmix`` nor ``levels`` is given in ``plot_kwargs``,
+    the colorbar bounds are inferred from the dataset in the top left panel,
+    which might lead to an inappropriate colorbar for the reference dataset
+    (top right panel). Thus, the use of the ``plot_kwargs`` ``vmin`` and
+    ``vmax`` or ``levels`` is highly recommend when using this ``common_cbar:
+    true``. This option has no effect if no reference dataset is given.
+fontsize: int, optional (default: 10)
+    Fontsize used for ticks, labels and titles. For the latter, use the given
+    fontsize plus 2. Does not affect suptitles.
+plot_func: str, optional (default: 'contourf')
+    Plot function used to plot the profiles. Must be a function of
+    :mod:`iris.plot` that supports plotting of 2D cubes with coordinates
+    latitude and height/air_pressure.
+plot_kwargs: dict, optional
+    Optional keyword arguments for the plot function defined by ``plot_func``.
+    Dictionary keys are elements identified by ``facet_used_for_labels`` or
+    ``default``, e.g., ``CMIP6`` if ``facet_used_for_labels: project`` or
+    ``historical`` if ``facet_used_for_labels: exp``. Dictionary values are
+    dictionaries used as keyword arguments for the plot function defined by
+    ``plot_func``. String arguments can include facets in curly brackets which
+    will be derived from the corresponding dataset, e.g., ``{project}``,
+    ``{short_name}``, ``{exp}``. Examples: ``default: {levels: 2}, CMIP6:
+    {vmin: 200, vmax: 250}``.
+plot_kwargs_bias: dict, optional
+    Optional keyword arguments for the plot function defined by ``plot_func``
+    for plotting biases. These keyword arguments update (and potentially
+    overwrite) the ``plot_kwargs`` for the bias plot. This option has no effect
+    if no reference dataset is given. See option ``plot_kwargs`` for more
+    details. By default, uses ``cmap: bwr``.
+pyplot_kwargs: dict, optional
+    Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
+    are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
+    single argument for these functions. String arguments can include facets in
+    curly brackets which will be derived from the corresponding dataset, e.g.,
+    ``{project}``, ``{short_name}``, ``{exp}``.  Examples: ``title: 'Awesome
+    Plot of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+rasterize: bool, optional (default: False)
+    If ``True``, use `rasterization
+    <https://matplotlib.org/stable/gallery/misc/rasterization_demo.html>`_ for
+    profile plots to produce smaller files. This is only relevant for vector
+    graphics (e.g., ``output_file_type=pdf,svg,ps``).
+show_y_minor_ticks: bool, optional (default: True)
+    Show minor ticks for time on the Y axis.
+show_x_minor_ticks: bool, optional (default: True)
+    Show minor ticks for latitude or longitude on the X axis.
+time_format: str, optional (default: None)
+    :func:`~datetime.datetime.strftime` format string that is used to format
+    the time axis using :class:`matplotlib.dates.DateFormatter`. If ``None``,
+    use the default formatting imposed by the iris plotting function.
+
 .. hint::
 
    Extra arguments given to the recipe are ignored, so it is safe to use yaml
@@ -367,6 +579,7 @@
 import cartopy.crs as ccrs
 import iris
 import matplotlib as mpl
+import matplotlib.dates as mdates
 import matplotlib.pyplot as plt
 import numpy as np
 import seaborn as sns
@@ -374,7 +587,12 @@
 from iris.coord_categorisation import add_year
 from iris.coords import AuxCoord
 from matplotlib.gridspec import GridSpec
-from matplotlib.ticker import FormatStrFormatter, LogLocator, NullFormatter
+from matplotlib.ticker import (
+    AutoMinorLocator,
+    FormatStrFormatter,
+    LogLocator,
+    NullFormatter,
+)
 from sklearn.metrics import r2_score
 
 import esmvaltool.diag_scripts.shared.iris_helpers as ih
@@ -397,16 +615,19 @@ def __init__(self, config):
         """Initialize class member."""
         super().__init__(config)
 
-        # Get default stettings
+        # Get default settings
         self.cfg = deepcopy(self.cfg)
         self.cfg.setdefault('facet_used_for_labels', 'dataset')
         self.cfg.setdefault('figure_kwargs', {'constrained_layout': True})
+        self.cfg.setdefault('group_variables_by', 'short_name')
         self.cfg.setdefault('savefig_kwargs', {
             'bbox_inches': 'tight',
             'dpi': 300,
             'orientation': 'landscape',
         })
         self.cfg.setdefault('seaborn_settings', {'style': 'ticks'})
+        logger.info("Using facet '%s' to group variables",
+                    self.cfg['group_variables_by'])
         logger.info("Using facet '%s' to create labels",
                     self.cfg['facet_used_for_labels'])
 
@@ -414,7 +635,7 @@ def __init__(self, config):
         self.input_data = self._load_and_preprocess_data()
         self.grouped_input_data = group_metadata(
             self.input_data,
-            'short_name',
+            self.cfg['group_variables_by'],
             sort=self.cfg['facet_used_for_labels'],
         )
 
@@ -436,7 +657,10 @@ def __init__(self, config):
             'annual_cycle',
             'map',
             'zonal_mean_profile',
-            '1d_profile'
+            '1d_profile',
+            'variable_vs_lat',
+            'hovmoeller_z_vs_time',
+            'hovmoeller_time_vs_lat_or_lon',
         ]
         for (plot_type, plot_options) in self.plots.items():
             if plot_type not in self.supported_plot_types:
@@ -453,14 +677,15 @@ def __init__(self, config):
                 self.plots[plot_type].setdefault('legend_kwargs', {})
                 self.plots[plot_type].setdefault('plot_kwargs', {})
                 self.plots[plot_type].setdefault('pyplot_kwargs', {})
+                self.plots[plot_type].setdefault('time_format', None)
 
-            if plot_type == 'annual_cycle':
+            elif plot_type == 'annual_cycle':
                 self.plots[plot_type].setdefault('gridline_kwargs', {})
                 self.plots[plot_type].setdefault('legend_kwargs', {})
                 self.plots[plot_type].setdefault('plot_kwargs', {})
                 self.plots[plot_type].setdefault('pyplot_kwargs', {})
 
-            if plot_type == 'map':
+            elif plot_type == 'map':
                 self.plots[plot_type].setdefault(
                     'cbar_label', '{short_name} [{units}]')
                 self.plots[plot_type].setdefault(
@@ -491,7 +716,7 @@ def __init__(self, config):
                 self.plots[plot_type].setdefault('x_pos_stats_avg', 0.0)
                 self.plots[plot_type].setdefault('x_pos_stats_bias', 0.92)
 
-            if plot_type == 'zonal_mean_profile':
+            elif plot_type == 'zonal_mean_profile':
                 self.plots[plot_type].setdefault(
                     'cbar_label', '{short_name} [{units}]')
                 self.plots[plot_type].setdefault(
@@ -518,7 +743,7 @@ def __init__(self, config):
                 self.plots[plot_type].setdefault('x_pos_stats_avg', 0.01)
                 self.plots[plot_type].setdefault('x_pos_stats_bias', 0.7)
 
-            if plot_type == '1d_profile':
+            elif plot_type == '1d_profile':
                 self.plots[plot_type].setdefault('aspect_ratio', 1.5)
                 self.plots[plot_type].setdefault('gridline_kwargs', {})
                 self.plots[plot_type].setdefault('legend_kwargs', {})
@@ -529,6 +754,63 @@ def __init__(self, config):
                 self.plots[plot_type].setdefault(
                     'show_y_minor_ticklabels', False
                 )
+            elif plot_type == 'variable_vs_lat':
+                self.plots[plot_type].setdefault('gridline_kwargs', {})
+                self.plots[plot_type].setdefault('legend_kwargs', {})
+                self.plots[plot_type].setdefault('plot_kwargs', {})
+                self.plots[plot_type].setdefault('pyplot_kwargs', {})
+
+            elif plot_type == 'hovmoeller_z_vs_time':
+                self.plots[plot_type].setdefault('cbar_label',
+                                                 '{short_name} [{units}]')
+                self.plots[plot_type].setdefault('cbar_label_bias',
+                                                 'Δ{short_name} [{units}]')
+                self.plots[plot_type].setdefault('cbar_kwargs',
+                                                 {'orientation': 'vertical'})
+                self.plots[plot_type].setdefault('cbar_kwargs_bias', {})
+                self.plots[plot_type].setdefault('common_cbar', False)
+                self.plots[plot_type].setdefault('fontsize', 10)
+                self.plots[plot_type].setdefault('log_y', True)
+                self.plots[plot_type].setdefault('plot_func', 'contourf')
+                self.plots[plot_type].setdefault('plot_kwargs', {})
+                self.plots[plot_type].setdefault('plot_kwargs_bias', {})
+                self.plots[plot_type]['plot_kwargs_bias'].setdefault(
+                    'cmap', 'bwr')
+                self.plots[plot_type].setdefault('pyplot_kwargs', {})
+                self.plots[plot_type].setdefault('rasterize', True)
+                self.plots[plot_type].setdefault('show_stats', True)
+                self.plots[plot_type].setdefault('show_y_minor_ticklabels',
+                                                 False)
+                self.plots[plot_type].setdefault('time_format', None)
+                self.plots[plot_type].setdefault('x_pos_stats_avg', 0.01)
+                self.plots[plot_type].setdefault('x_pos_stats_bias', 0.7)
+
+            elif plot_type == 'hovmoeller_time_vs_lat_or_lon':
+                self.plots[plot_type].setdefault(
+                    'cbar_label', '{short_name} [{units}]')
+                self.plots[plot_type].setdefault(
+                    'cbar_label_bias', 'Δ{short_name} [{units}]')
+                self.plots[plot_type].setdefault(
+                    'cbar_kwargs', {'orientation': 'vertical'}
+                )
+                self.plots[plot_type].setdefault('cbar_kwargs_bias', {})
+                self.plots[plot_type].setdefault('common_cbar', False)
+                self.plots[plot_type].setdefault('fontsize', 10)
+                self.plots[plot_type].setdefault('plot_func', 'contourf')
+                self.plots[plot_type].setdefault('plot_kwargs', {})
+                self.plots[plot_type].setdefault('plot_kwargs_bias', {})
+                self.plots[plot_type]['plot_kwargs_bias'].setdefault(
+                    'cmap', 'bwr'
+                )
+                self.plots[plot_type].setdefault('pyplot_kwargs', {})
+                self.plots[plot_type].setdefault('rasterize', False)
+                self.plots[plot_type].setdefault(
+                    'show_y_minor_ticks', True
+                )
+                self.plots[plot_type].setdefault(
+                    'show_x_minor_ticks', True
+                )
+                self.plots[plot_type].setdefault('time_format', None)
 
         # Check that facet_used_for_labels is present for every dataset
         for dataset in self.input_data:
@@ -587,10 +869,10 @@ def _add_stats(self, plot_type, axes, dim_coords, dataset,
         # Different options for the different plots types
         fontsize = 6.0
         y_pos = 0.95
-        if plot_type == 'map':
-            x_pos_bias = self.plots[plot_type]['x_pos_stats_bias']
-            x_pos = self.plots[plot_type]['x_pos_stats_avg']
-        elif plot_type in ['zonal_mean_profile']:
+        if all([
+                'x_pos_stats_avg' in self.plots[plot_type],
+                'x_pos_stats_bias' in self.plots[plot_type],
+        ]):
             x_pos_bias = self.plots[plot_type]['x_pos_stats_bias']
             x_pos = self.plots[plot_type]['x_pos_stats_avg']
         else:
@@ -757,7 +1039,8 @@ def _get_plot_kwargs(self, plot_type, dataset, bias=False):
                 plot_kwargs[key] = val
 
         # Default settings for different plot types
-        if plot_type in ('timeseries', 'annual_cycle', '1d_profile'):
+        if plot_type in ('timeseries', 'annual_cycle', '1d_profile',
+                         'variable_vs_lat'):
             plot_kwargs.setdefault('label', label)
 
         return deepcopy(plot_kwargs)
@@ -822,6 +1105,7 @@ def _plot_map_with_ref(self, plot_func, dataset, ref_dataset):
                 axes_data.gridlines(**gridline_kwargs)
             axes_data.set_title(self._get_label(dataset), pad=3.0)
             self._add_stats(plot_type, axes_data, dim_coords_dat, dataset)
+            self._process_pyplot_kwargs(plot_type, dataset)
 
             # Plot reference dataset (top right)
             # Note: make sure to use the same vmin and vmax than the top left
@@ -838,6 +1122,7 @@ def _plot_map_with_ref(self, plot_func, dataset, ref_dataset):
                 axes_ref.gridlines(**gridline_kwargs)
             axes_ref.set_title(self._get_label(ref_dataset), pad=3.0)
             self._add_stats(plot_type, axes_ref, dim_coords_ref, ref_dataset)
+            self._process_pyplot_kwargs(plot_type, ref_dataset)
 
             # Add colorbar(s)
             self._add_colorbar(plot_type, plot_data, plot_ref, axes_data,
@@ -981,6 +1266,7 @@ def _plot_zonal_mean_profile_with_ref(self, plot_func, dataset,
             else:
                 axes_data.get_yaxis().set_minor_formatter(NullFormatter())
             self._add_stats(plot_type, axes_data, dim_coords_dat, dataset)
+            self._process_pyplot_kwargs(plot_type, dataset)
 
             # Plot reference dataset (top right)
             # Note: make sure to use the same vmin and vmax than the top left
@@ -995,6 +1281,7 @@ def _plot_zonal_mean_profile_with_ref(self, plot_func, dataset,
             axes_ref.set_title(self._get_label(ref_dataset), pad=3.0)
             plt.setp(axes_ref.get_yticklabels(), visible=False)
             self._add_stats(plot_type, axes_ref, dim_coords_ref, ref_dataset)
+            self._process_pyplot_kwargs(plot_type, ref_dataset)
 
             # Add colorbar(s)
             self._add_colorbar(plot_type, plot_data, plot_ref, axes_data,
@@ -1105,50 +1392,402 @@ def _plot_zonal_mean_profile_without_ref(self, plot_func, dataset):
 
         return (plot_path, {netcdf_path: cube})
 
-    def _process_pyplot_kwargs(self, plot_type, dataset):
-        """Process functions for :mod:`matplotlib.pyplot`."""
-        pyplot_kwargs = self.plots[plot_type]['pyplot_kwargs']
-        for (func, arg) in pyplot_kwargs.items():
-            if isinstance(arg, str):
-                arg = self._fill_facet_placeholders(
-                    arg,
-                    dataset,
-                    f"pyplot_kwargs of {plot_type} '{func}: {arg}'",
-                )
-            if arg is None:
-                getattr(plt, func)()
-            else:
-                getattr(plt, func)(arg)
-
-    @staticmethod
-    def _check_cube_dimensions(cube, plot_type):
-        """Check that cube has correct dimensional variables."""
-        expected_dimensions_dict = {
-            'annual_cycle': (['month_number'],),
-            'map': (['latitude', 'longitude'],),
-            'zonal_mean_profile': (['latitude', 'air_pressure'],
-                                   ['latitude', 'altitude']),
-            'timeseries': (['time'],),
-            '1d_profile': (['air_pressure'],
-                           ['altitude']),
+    def _plot_hovmoeller_z_vs_time_without_ref(self, plot_func, dataset):
+        """Plot Hovmoeller Z vs. time for single dataset without reference."""
+        plot_type = 'hovmoeller_z_vs_time'
+        logger.info(
+            "Plotting Hovmoeller Z vs. time without reference dataset"
+            " for '%s'", self._get_label(dataset))
 
-        }
-        if plot_type not in expected_dimensions_dict:
-            raise NotImplementedError(f"plot_type '{plot_type}' not supported")
-        expected_dimensions = expected_dimensions_dict[plot_type]
-        for dims in expected_dimensions:
-            cube_dims = [cube.coords(dim, dim_coords=True) for dim in dims]
-            if all(cube_dims) and cube.ndim == len(dims):
-                return dims
-        expected_dims_str = ' or '.join(
-            [str(dims) for dims in expected_dimensions]
-        )
-        raise ValueError(
-            f"Expected cube that exactly has the dimensional coordinates "
-            f"{expected_dims_str}, got {cube.summary(shorten=True)}")
+        # Make sure that the data has the correct dimensions
+        cube = dataset['cube']
+        dim_coords_dat = self._check_cube_dimensions(cube, plot_type)
 
-    @staticmethod
-    def _fill_facet_placeholders(string, dataset, description):
+        # Create plot with desired settings
+        with mpl.rc_context(self._get_custom_mpl_rc_params(plot_type)):
+            fig = plt.figure(**self.cfg['figure_kwargs'])
+            axes = fig.add_subplot()
+            plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
+            plot_kwargs['axes'] = axes
+            plot_hovmoeller = plot_func(cube, **plot_kwargs)
+
+            # Print statistics if desired
+            self._add_stats(plot_type, axes, dim_coords_dat, dataset)
+
+            # Setup colorbar
+            fontsize = self.plots[plot_type]['fontsize']
+            colorbar = fig.colorbar(plot_hovmoeller,
+                                    ax=axes,
+                                    **self._get_cbar_kwargs(plot_type))
+            colorbar.set_label(self._get_cbar_label(plot_type, dataset),
+                               fontsize=fontsize)
+            colorbar.ax.tick_params(labelsize=fontsize)
+
+            # Customize plot
+            axes.set_title(self._get_label(dataset))
+            fig.suptitle(f"{dataset['long_name']} ({dataset['start_year']}-"
+                         f"{dataset['end_year']})")
+            z_coord = cube.coord(axis='Z')
+            axes.set_ylabel(f'{z_coord.long_name} [{z_coord.units}]')
+            if self.plots[plot_type]['log_y']:
+                axes.set_yscale('log')
+                axes.get_yaxis().set_major_formatter(
+                    FormatStrFormatter('%.1f'))
+            if self.plots[plot_type]['show_y_minor_ticklabels']:
+                axes.get_yaxis().set_minor_formatter(
+                    FormatStrFormatter('%.1f'))
+            else:
+                axes.get_yaxis().set_minor_formatter(NullFormatter())
+            if self.plots[plot_type]['time_format'] is not None:
+                axes.get_xaxis().set_major_formatter(
+                    mdates.DateFormatter(self.plots[plot_type]['time_format']))
+            axes.set_xlabel('time')
+            self._process_pyplot_kwargs(plot_type, dataset)
+
+            # Rasterization
+            if self.plots[plot_type]['rasterize']:
+                self._set_rasterized([axes])
+
+        # File paths
+        plot_path = self.get_plot_path(plot_type, dataset)
+        netcdf_path = get_diagnostic_filename(Path(plot_path).stem, self.cfg)
+
+        return (plot_path, {netcdf_path: cube})
+
+    def _plot_hovmoeller_z_vs_time_with_ref(self, plot_func, dataset,
+                                            ref_dataset):
+        """Plot Hovmoeller Z vs. time for single dataset with reference."""
+        plot_type = 'hovmoeller_z_vs_time'
+        logger.info(
+            "Plotting Hovmoeller z vs. time with reference dataset"
+            " '%s' for '%s'", self._get_label(ref_dataset),
+            self._get_label(dataset))
+
+        # Make sure that the data has the correct dimensions
+        cube = dataset['cube']
+        ref_cube = ref_dataset['cube']
+        dim_coords_dat = self._check_cube_dimensions(cube, plot_type)
+        dim_coords_ref = self._check_cube_dimensions(ref_cube, plot_type)
+
+        # Create single figure with multiple axes
+        with mpl.rc_context(self._get_custom_mpl_rc_params(plot_type)):
+            fig = plt.figure(**self.cfg['figure_kwargs'])
+            gridspec = GridSpec(5,
+                                4,
+                                figure=fig,
+                                height_ratios=[1.0, 1.0, 0.4, 1.0, 1.0])
+
+            # Options used for all subplots
+            plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
+            fontsize = self.plots[plot_type]['fontsize']
+
+            # Plot dataset (top left)
+            axes_data = fig.add_subplot(gridspec[0:2, 0:2])
+            plot_kwargs['axes'] = axes_data
+            plot_data = plot_func(cube, **plot_kwargs)
+            axes_data.set_title(self._get_label(dataset), pad=3.0)
+            z_coord = cube.coord(axis='Z')
+            axes_data.set_ylabel(f'{z_coord.long_name} [{z_coord.units}]')
+            if self.plots[plot_type]['log_y']:
+                axes_data.set_yscale('log')
+                axes_data.get_yaxis().set_major_formatter(
+                    FormatStrFormatter('%.1f'))
+            if self.plots[plot_type]['show_y_minor_ticklabels']:
+                axes_data.get_yaxis().set_minor_formatter(
+                    FormatStrFormatter('%.1f'))
+            else:
+                axes_data.get_yaxis().set_minor_formatter(NullFormatter())
+            if self.plots[plot_type]['time_format'] is not None:
+                axes_data.get_xaxis().set_major_formatter(
+                    mdates.DateFormatter(self.plots[plot_type]['time_format']))
+            self._add_stats(plot_type, axes_data, dim_coords_dat, dataset)
+            self._process_pyplot_kwargs(plot_type, dataset)
+
+            # Plot reference dataset (top right)
+            # Note: make sure to use the same vmin and vmax than the top left
+            # plot if a common colorbar is desired
+            axes_ref = fig.add_subplot(gridspec[0:2, 2:4],
+                                       sharex=axes_data,
+                                       sharey=axes_data)
+            plot_kwargs['axes'] = axes_ref
+            if self.plots[plot_type]['common_cbar']:
+                plot_kwargs.setdefault('vmin', plot_data.get_clim()[0])
+                plot_kwargs.setdefault('vmax', plot_data.get_clim()[1])
+            plot_ref = plot_func(ref_cube, **plot_kwargs)
+            axes_ref.set_title(self._get_label(ref_dataset), pad=3.0)
+            plt.setp(axes_ref.get_yticklabels(), visible=False)
+            self._add_stats(plot_type, axes_ref, dim_coords_ref, ref_dataset)
+            self._process_pyplot_kwargs(plot_type, ref_dataset)
+
+            # Add colorbar(s)
+            self._add_colorbar(plot_type, plot_data, plot_ref, axes_data,
+                               axes_ref, dataset, ref_dataset)
+
+            # Plot bias (bottom center)
+            bias_cube = cube - ref_cube
+            axes_bias = fig.add_subplot(gridspec[3:5, 1:3],
+                                        sharex=axes_data,
+                                        sharey=axes_data)
+            plot_kwargs_bias = self._get_plot_kwargs(plot_type,
+                                                     dataset,
+                                                     bias=True)
+            plot_kwargs_bias['axes'] = axes_bias
+            plot_bias = plot_func(bias_cube, **plot_kwargs_bias)
+            axes_bias.set_title(
+                f"{self._get_label(dataset)} - {self._get_label(ref_dataset)}",
+                pad=3.0,
+            )
+            axes_bias.set_xlabel('time')
+            axes_bias.set_ylabel(f'{z_coord.long_name} [{z_coord.units}]')
+            cbar_kwargs_bias = self._get_cbar_kwargs(plot_type, bias=True)
+            cbar_bias = fig.colorbar(plot_bias,
+                                     ax=axes_bias,
+                                     **cbar_kwargs_bias)
+            cbar_bias.set_label(
+                self._get_cbar_label(plot_type, dataset, bias=True),
+                fontsize=fontsize,
+            )
+            cbar_bias.ax.tick_params(labelsize=fontsize)
+            self._add_stats(plot_type, axes_bias, dim_coords_dat, dataset,
+                            ref_dataset)
+
+            # Customize plot
+            fig.suptitle(f"{dataset['long_name']} ({dataset['start_year']}-"
+                         f"{dataset['end_year']})")
+            self._process_pyplot_kwargs(plot_type, dataset)
+
+            # Rasterization
+            if self.plots[plot_type]['rasterize']:
+                self._set_rasterized([axes_data, axes_ref, axes_bias])
+
+        # File paths
+        plot_path = self.get_plot_path(plot_type, dataset)
+        netcdf_path = (get_diagnostic_filename(
+            Path(plot_path).stem + "_{pos}", self.cfg))
+        netcdf_paths = {
+            netcdf_path.format(pos='top_left'): cube,
+            netcdf_path.format(pos='top_right'): ref_cube,
+            netcdf_path.format(pos='bottom'): bias_cube,
+        }
+
+        return (plot_path, netcdf_paths)
+
+    def _plot_hovmoeller_time_vs_lat_or_lon_with_ref(self, plot_func, dataset,
+                                                     ref_dataset):
+        """Plot the hovmoeller profile for single dataset with reference."""
+        plot_type = 'hovmoeller_time_vs_lat_or_lon'
+        logger.info("Plotting Hovmoeller plots with reference dataset"
+                    " '%s' for '%s'",
+                    self._get_label(ref_dataset), self._get_label(dataset))
+
+        # Make sure that the data has the correct dimensions
+        cube = dataset['cube']
+        ref_cube = ref_dataset['cube']
+        dim_coords_dat = self._check_cube_dimensions(cube, plot_type)
+        self._check_cube_dimensions(ref_cube, plot_type)
+
+        # Create single figure with multiple axes
+        with mpl.rc_context(self._get_custom_mpl_rc_params(plot_type)):
+            fig = plt.figure(**self.cfg['figure_kwargs'])
+            gridspec = GridSpec(5, 4, figure=fig,
+                                height_ratios=[1.0, 1.0, 0.4, 1.0, 1.0])
+
+            # Options used for all subplots
+            plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
+            fontsize = self.plots[plot_type]['fontsize']
+
+            # Plot dataset (top left)
+            axes_data = fig.add_subplot(gridspec[0:2, 0:2])
+            plot_kwargs['axes'] = axes_data
+            coord_names = [coord[0].name() for coord in cube.dim_coords]
+            if coord_names[0] == "time":
+                coord_names.reverse()
+            plot_kwargs['coords'] = coord_names
+            plot_data = plot_func(cube, **plot_kwargs)
+            axes_data.set_title(self._get_label(dataset), pad=3.0)
+            axes_data.set_ylabel('time')
+            if self.plots[plot_type]['time_format'] is not None:
+                axes_data.get_yaxis().set_major_formatter(mdates.DateFormatter(
+                    self.plots[plot_type]['time_format']))
+            if self.plots[plot_type]['show_y_minor_ticks']:
+                axes_data.get_yaxis().set_minor_locator(AutoMinorLocator())
+            if self.plots[plot_type]['show_x_minor_ticks']:
+                axes_data.get_xaxis().set_minor_locator(AutoMinorLocator())
+            self._process_pyplot_kwargs(plot_type, dataset)
+
+            # Plot reference dataset (top right)
+            # Note: make sure to use the same vmin and vmax than the top left
+            # plot if a common colorbar is desired
+            axes_ref = fig.add_subplot(gridspec[0:2, 2:4], sharex=axes_data,
+                                       sharey=axes_data)
+            plot_kwargs['axes'] = axes_ref
+            if self.plots[plot_type]['common_cbar']:
+                plot_kwargs.setdefault('vmin', plot_data.get_clim()[0])
+                plot_kwargs.setdefault('vmax', plot_data.get_clim()[1])
+            plot_ref = plot_func(ref_cube, **plot_kwargs)
+            axes_ref.set_title(self._get_label(ref_dataset), pad=3.0)
+            plt.setp(axes_ref.get_yticklabels(), visible=False)
+            self._process_pyplot_kwargs(plot_type, ref_dataset)
+
+            # Add colorbar(s)
+            self._add_colorbar(plot_type, plot_data, plot_ref, axes_data,
+                               axes_ref, dataset, ref_dataset)
+
+            # Plot bias (bottom center)
+            bias_cube = cube - ref_cube
+            axes_bias = fig.add_subplot(gridspec[3:5, 1:3], sharex=axes_data,
+                                        sharey=axes_data)
+            plot_kwargs_bias = self._get_plot_kwargs(plot_type, dataset,
+                                                     bias=True)
+            plot_kwargs_bias['axes'] = axes_bias
+            plot_kwargs_bias['coords'] = coord_names
+            plot_bias = plot_func(bias_cube, **plot_kwargs_bias)
+            axes_bias.set_title(
+                f"{self._get_label(dataset)} - {self._get_label(ref_dataset)}",
+                pad=3.0,
+            )
+            axes_bias.set_ylabel('time')
+            if 'latitude' in dim_coords_dat:
+                axes_bias.set_xlabel('latitude [°N]')
+            elif 'longitude' in dim_coords_dat:
+                axes_bias.set_xlabel('longitude [°E]')
+            cbar_kwargs_bias = self._get_cbar_kwargs(plot_type, bias=True)
+            cbar_bias = fig.colorbar(plot_bias, ax=axes_bias,
+                                     **cbar_kwargs_bias)
+            cbar_bias.set_label(
+                self._get_cbar_label(plot_type, dataset, bias=True),
+                fontsize=fontsize,
+            )
+            cbar_bias.ax.tick_params(labelsize=fontsize)
+
+            # Customize plot
+            fig.suptitle(f"{dataset['long_name']} ({dataset['start_year']}-"
+                         f"{dataset['end_year']})")
+            self._process_pyplot_kwargs(plot_type, dataset)
+
+            # Rasterization
+            if self.plots[plot_type]['rasterize']:
+                self._set_rasterized([axes_data, axes_ref, axes_bias])
+
+        # File paths
+        plot_path = self.get_plot_path(plot_type, dataset)
+        netcdf_path = (
+            get_diagnostic_filename(Path(plot_path).stem + "_{pos}", self.cfg)
+        )
+        netcdf_paths = {
+            netcdf_path.format(pos='top_left'): cube,
+            netcdf_path.format(pos='top_right'): ref_cube,
+            netcdf_path.format(pos='bottom'): bias_cube,
+        }
+
+        return (plot_path, netcdf_paths)
+
+    def _plot_hovmoeller_time_vs_lat_or_lon_without_ref(self, plot_func,
+                                                        dataset):
+        """Plot time vs zonal or meridional Hovmoeller without reference."""
+        plot_type = 'hovmoeller_time_vs_lat_or_lon'
+        logger.info("Plotting Hovmoeller plots without reference dataset"
+                    " for '%s'", self._get_label(dataset))
+
+        # Make sure that the data has the correct dimensions
+        cube = dataset['cube']
+        dim_coords_dat = self._check_cube_dimensions(cube, plot_type)
+
+        # Create plot with desired settings
+        with mpl.rc_context(self._get_custom_mpl_rc_params(plot_type)):
+            fig = plt.figure(**self.cfg['figure_kwargs'])
+            axes = fig.add_subplot()
+            plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
+            plot_kwargs['axes'] = axes
+
+            # Make sure time is on y-axis
+            plot_kwargs['coords'] = list(reversed(dim_coords_dat))
+            plot_hovmoeller = plot_func(cube, **plot_kwargs)
+
+            # Setup colorbar
+            fontsize = self.plots[plot_type]['fontsize']
+            colorbar = fig.colorbar(plot_hovmoeller, ax=axes,
+                                    **self._get_cbar_kwargs(plot_type))
+            colorbar.set_label(self._get_cbar_label(plot_type, dataset),
+                               fontsize=fontsize)
+            colorbar.ax.tick_params(labelsize=fontsize)
+
+            # Customize plot
+            axes.set_title(self._get_label(dataset))
+            fig.suptitle(f"{dataset['long_name']} ({dataset['start_year']}-"
+                         f"{dataset['end_year']})")
+            if 'latitude' in dim_coords_dat:
+                axes.set_xlabel('latitude [°N]')
+            elif 'longitude' in dim_coords_dat:
+                axes.set_xlabel('longitude [°E]')
+            axes.set_ylabel('time')
+            if self.plots[plot_type]['time_format'] is not None:
+                axes.get_yaxis().set_major_formatter(mdates.DateFormatter(
+                    self.plots[plot_type]['time_format'])
+                )
+            if self.plots[plot_type]['show_y_minor_ticks']:
+                axes.get_yaxis().set_minor_locator(AutoMinorLocator())
+            if self.plots[plot_type]['show_x_minor_ticks']:
+                axes.get_xaxis().set_minor_locator(AutoMinorLocator())
+            self._process_pyplot_kwargs(plot_type, dataset)
+
+            # Rasterization
+            if self.plots[plot_type]['rasterize']:
+                self._set_rasterized([axes])
+
+        # File paths
+        plot_path = self.get_plot_path(plot_type, dataset)
+        netcdf_path = get_diagnostic_filename(Path(plot_path).stem, self.cfg)
+        return (plot_path, {netcdf_path: cube})
+
+    def _process_pyplot_kwargs(self, plot_type, dataset):
+        """Process functions for :mod:`matplotlib.pyplot`."""
+        pyplot_kwargs = self.plots[plot_type]['pyplot_kwargs']
+        for (func, arg) in pyplot_kwargs.items():
+            if isinstance(arg, str):
+                arg = self._fill_facet_placeholders(
+                    arg,
+                    dataset,
+                    f"pyplot_kwargs of {plot_type} '{func}: {arg}'",
+                )
+            if arg is None:
+                getattr(plt, func)()
+            else:
+                getattr(plt, func)(arg)
+
+    @staticmethod
+    def _check_cube_dimensions(cube, plot_type):
+        """Check that cube has correct dimensional variables."""
+        expected_dimensions_dict = {
+            'annual_cycle': (['month_number'],),
+            'map': (['latitude', 'longitude'],),
+            'zonal_mean_profile': (['latitude', 'air_pressure'],
+                                   ['latitude', 'altitude']),
+            'timeseries': (['time'],),
+            '1d_profile': (['air_pressure'],
+                           ['altitude']),
+            'variable_vs_lat': (['latitude'],),
+            'hovmoeller_z_vs_time': (['time', 'air_pressure'],
+                                     ['time', 'altitude']),
+            'hovmoeller_time_vs_lat_or_lon': (['time', 'latitude'],
+                                              ['time', 'longitude']),
+        }
+        if plot_type not in expected_dimensions_dict:
+            raise NotImplementedError(f"plot_type '{plot_type}' not supported")
+        expected_dimensions = expected_dimensions_dict[plot_type]
+        for dims in expected_dimensions:
+            cube_dims = [cube.coords(dim, dim_coords=True) for dim in dims]
+            if all(cube_dims) and cube.ndim == len(dims):
+                return dims
+        expected_dims_str = ' or '.join(
+            [str(dims) for dims in expected_dimensions]
+        )
+        raise ValueError(
+            f"Expected cube that exactly has the dimensional coordinates "
+            f"{expected_dims_str}, got {cube.summary(shorten=True)}")
+
+    @staticmethod
+    def _fill_facet_placeholders(string, dataset, description):
         """Fill facet placeholders."""
         try:
             string = string.format(**dataset)
@@ -1170,21 +1809,21 @@ def _get_multi_dataset_facets(datasets):
                 multi_dataset_facets[key] = f'ambiguous_{key}'
         return multi_dataset_facets
 
-    @staticmethod
-    def _get_reference_dataset(datasets, short_name):
+    def _get_reference_dataset(self, datasets):
         """Extract reference dataset."""
+        variable = datasets[0][self.cfg['group_variables_by']]
         ref_datasets = [d for d in datasets if
                         d.get('reference_for_monitor_diags', False)]
         if len(ref_datasets) > 1:
             raise ValueError(
                 f"Expected at most 1 reference dataset (with "
                 f"'reference_for_monitor_diags: true' for variable "
-                f"'{short_name}', got {len(ref_datasets):d}")
+                f"'{variable}', got {len(ref_datasets):d}")
         if ref_datasets:
             return ref_datasets[0]
         return None
 
-    def create_timeseries_plot(self, datasets, short_name):
+    def create_timeseries_plot(self, datasets):
         """Create time series plot."""
         plot_type = 'timeseries'
         if plot_type not in self.plots:
@@ -1226,8 +1865,15 @@ def create_timeseries_plot(self, datasets, short_name):
         # Default plot appearance
         multi_dataset_facets = self._get_multi_dataset_facets(datasets)
         axes.set_title(multi_dataset_facets['long_name'])
-        axes.set_xlabel('Time')
-        axes.set_ylabel(f"{short_name} [{multi_dataset_facets['units']}]")
+        axes.set_xlabel('time')
+        # apply time formatting
+        if self.plots[plot_type]['time_format'] is not None:
+            axes.get_xaxis().set_major_formatter(
+                mdates.DateFormatter(self.plots[plot_type]['time_format']))
+        axes.set_ylabel(
+            f"{multi_dataset_facets[self.cfg['group_variables_by']]} "
+            f"[{multi_dataset_facets['units']}]"
+        )
         gridline_kwargs = self._get_gridline_kwargs(plot_type)
         if gridline_kwargs is not False:
             axes.grid(**gridline_kwargs)
@@ -1267,7 +1913,7 @@ def create_timeseries_plot(self, datasets, short_name):
             provenance_logger.log(plot_path, provenance_record)
             provenance_logger.log(netcdf_path, provenance_record)
 
-    def create_annual_cycle_plot(self, datasets, short_name):
+    def create_annual_cycle_plot(self, datasets):
         """Create annual cycle plot."""
         plot_type = 'annual_cycle'
         if plot_type not in self.plots:
@@ -1298,7 +1944,10 @@ def create_annual_cycle_plot(self, datasets, short_name):
         multi_dataset_facets = self._get_multi_dataset_facets(datasets)
         axes.set_title(multi_dataset_facets['long_name'])
         axes.set_xlabel('Month')
-        axes.set_ylabel(f"{short_name} [{multi_dataset_facets['units']}]")
+        axes.set_ylabel(
+            f"{multi_dataset_facets[self.cfg['group_variables_by']]} "
+            f"[{multi_dataset_facets['units']}]"
+        )
         axes.set_xticks(range(1, 13), [str(m) for m in range(1, 13)])
         gridline_kwargs = self._get_gridline_kwargs(plot_type)
         if gridline_kwargs is not False:
@@ -1339,7 +1988,7 @@ def create_annual_cycle_plot(self, datasets, short_name):
             provenance_logger.log(plot_path, provenance_record)
             provenance_logger.log(netcdf_path, provenance_record)
 
-    def create_map_plot(self, datasets, short_name):
+    def create_map_plot(self, datasets):
         """Create map plot."""
         plot_type = 'map'
         if plot_type not in self.plots:
@@ -1349,7 +1998,7 @@ def create_map_plot(self, datasets, short_name):
             raise ValueError(f"No input data to plot '{plot_type}' given")
 
         # Get reference dataset if possible
-        ref_dataset = self._get_reference_dataset(datasets, short_name)
+        ref_dataset = self._get_reference_dataset(datasets)
         if ref_dataset is None:
             logger.info("Plotting %s without reference dataset", plot_type)
         else:
@@ -1415,7 +2064,7 @@ def create_map_plot(self, datasets, short_name):
                 for netcdf_path in netcdf_paths:
                     provenance_logger.log(netcdf_path, provenance_record)
 
-    def create_zonal_mean_profile_plot(self, datasets, short_name):
+    def create_zonal_mean_profile_plot(self, datasets):
         """Create zonal mean profile plot."""
         plot_type = 'zonal_mean_profile'
         if plot_type not in self.plots:
@@ -1425,7 +2074,7 @@ def create_zonal_mean_profile_plot(self, datasets, short_name):
             raise ValueError(f"No input data to plot '{plot_type}' given")
 
         # Get reference dataset if possible
-        ref_dataset = self._get_reference_dataset(datasets, short_name)
+        ref_dataset = self._get_reference_dataset(datasets)
         if ref_dataset is None:
             logger.info("Plotting %s without reference dataset", plot_type)
         else:
@@ -1493,7 +2142,7 @@ def create_zonal_mean_profile_plot(self, datasets, short_name):
                 for netcdf_path in netcdf_paths:
                     provenance_logger.log(netcdf_path, provenance_record)
 
-    def create_1d_profile_plot(self, datasets, short_name):
+    def create_1d_profile_plot(self, datasets):
         """Create 1D profile plot."""
         plot_type = '1d_profile'
         if plot_type not in self.plots:
@@ -1525,7 +2174,10 @@ def create_1d_profile_plot(self, datasets, short_name):
 
         # Default plot appearance
         axes.set_title(multi_dataset_facets['long_name'])
-        axes.set_xlabel(f"{short_name} [{multi_dataset_facets['units']}]")
+        axes.set_xlabel(
+            f"{multi_dataset_facets[self.cfg['group_variables_by']]} "
+            f"[{multi_dataset_facets['units']}]"
+        )
         z_coord = cube.coord(axis='Z')
         axes.set_ylabel(f'{z_coord.long_name} [{z_coord.units}]')
 
@@ -1595,15 +2247,239 @@ def create_1d_profile_plot(self, datasets, short_name):
             provenance_logger.log(plot_path, provenance_record)
             provenance_logger.log(netcdf_path, provenance_record)
 
+    def create_variable_vs_lat_plot(self, datasets):
+        """Create Variable as a function of latitude."""
+        plot_type = 'variable_vs_lat'
+        if plot_type not in self.plots:
+            return
+        if not datasets:
+            raise ValueError(f"No input data to plot '{plot_type}' given")
+        logger.info("Plotting %s", plot_type)
+        fig = plt.figure(**self.cfg['figure_kwargs'])
+        axes = fig.add_subplot()
+
+        # Plot all datasets in one single figure
+        ancestors = []
+        cubes = {}
+        for dataset in datasets:
+            ancestors.append(dataset['filename'])
+            cube = dataset['cube']
+            cubes[self._get_label(dataset)] = cube
+            self._check_cube_dimensions(cube, plot_type)
+
+            # Plot data
+            plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
+            plot_kwargs['axes'] = axes
+            iris.plot.plot(cube, **plot_kwargs)
+
+        # Default plot appearance
+        multi_dataset_facets = self._get_multi_dataset_facets(datasets)
+        axes.set_title(multi_dataset_facets['long_name'])
+        axes.set_xlabel('latitude [°N]')
+        axes.set_ylabel(
+            f"{multi_dataset_facets[self.cfg['group_variables_by']]} "
+            f"[{multi_dataset_facets['units']}]"
+        )
+        gridline_kwargs = self._get_gridline_kwargs(plot_type)
+        if gridline_kwargs is not False:
+            axes.grid(**gridline_kwargs)
+
+        # Legend
+        legend_kwargs = self.plots[plot_type]['legend_kwargs']
+        if legend_kwargs is not False:
+            axes.legend(**legend_kwargs)
+
+        # Customize plot appearance
+        self._process_pyplot_kwargs(plot_type, multi_dataset_facets)
+
+        # Save plot
+        plot_path = self.get_plot_path(plot_type, multi_dataset_facets)
+        fig.savefig(plot_path, **self.cfg['savefig_kwargs'])
+        logger.info("Wrote %s", plot_path)
+        plt.close()
+
+        # Save netCDF file
+        netcdf_path = get_diagnostic_filename(Path(plot_path).stem, self.cfg)
+        var_attrs = {
+            n: datasets[0][n] for n in ('short_name', 'long_name', 'units')
+        }
+        io.save_1d_data(cubes, netcdf_path, 'latitude', var_attrs)
+
+        # Provenance tracking
+        caption = (f"{multi_dataset_facets['long_name']} vs. latitude for "
+                   f"various datasets.")
+        provenance_record = {
+            'ancestors': ancestors,
+            'authors': ['sarauer_ellen'],
+            'caption': caption,
+            'plot_types': ['line'],
+            'long_names': [var_attrs['long_name']],
+        }
+        with ProvenanceLogger(self.cfg) as provenance_logger:
+            provenance_logger.log(plot_path, provenance_record)
+            provenance_logger.log(netcdf_path, provenance_record)
+
+    def create_hovmoeller_z_vs_time_plot(self, datasets):
+        """Create Hovmoeller Z vs. time plot."""
+        plot_type = 'hovmoeller_z_vs_time'
+        if plot_type not in self.plots:
+            return
+
+        if not datasets:
+            raise ValueError(f"No input data to plot '{plot_type}' given")
+
+        # Get reference dataset if possible
+        ref_dataset = self._get_reference_dataset(datasets)
+        if ref_dataset is None:
+            logger.info("Plotting %s without reference dataset", plot_type)
+        else:
+            logger.info("Plotting %s with reference dataset '%s'", plot_type,
+                        self._get_label(ref_dataset))
+
+        # Get plot function
+        plot_func = self._get_plot_func(plot_type)
+
+        # Create a single plot for each dataset (incl. reference dataset if
+        # given)
+        for dataset in datasets:
+            if dataset == ref_dataset:
+                continue
+            ancestors = [dataset['filename']]
+            if ref_dataset is None:
+                (plot_path,
+                 netcdf_paths) = (self._plot_hovmoeller_z_vs_time_without_ref(
+                     plot_func, dataset))
+                caption = (
+                    f"Hovmoeller Z vs. time plot of {dataset['long_name']} "
+                    f"of dataset "
+                    f"{dataset['dataset']} (project {dataset['project']}) "
+                    f"from {dataset['start_year']} to {dataset['end_year']}.")
+            else:
+                (plot_path,
+                 netcdf_paths) = (self._plot_hovmoeller_z_vs_time_with_ref(
+                     plot_func, dataset, ref_dataset))
+                caption = (
+                    f"Hovmoeller Z vs. time plot of {dataset['long_name']} "
+                    f"of dataset "
+                    f"{dataset['dataset']} (project {dataset['project']}) "
+                    f"including bias relative to {ref_dataset['dataset']} "
+                    f"(project {ref_dataset['project']}) from "
+                    f"{dataset['start_year']} to {dataset['end_year']}.")
+                ancestors.append(ref_dataset['filename'])
+
+            # If statistics are shown add a brief description to the caption
+            if self.plots[plot_type]['show_stats']:
+                caption += (
+                    " The number in the top left corner corresponds to the "
+                    "spatiotemporal mean.")
+
+            # Save plot
+            plt.savefig(plot_path, **self.cfg['savefig_kwargs'])
+            logger.info("Wrote %s", plot_path)
+            plt.close()
+
+            # Save netCDFs
+            for (netcdf_path, cube) in netcdf_paths.items():
+                io.iris_save(cube, netcdf_path)
+
+            # Provenance tracking
+            provenance_record = {
+                'ancestors': ancestors,
+                'authors': ['kuehbacher_birgit', 'heuer_helge'],
+                'caption': caption,
+                'plot_types': ['vert'],
+                'long_names': [dataset['long_name']],
+            }
+            with ProvenanceLogger(self.cfg) as provenance_logger:
+                provenance_logger.log(plot_path, provenance_record)
+                for netcdf_path in netcdf_paths:
+                    provenance_logger.log(netcdf_path, provenance_record)
+
+    def create_hovmoeller_time_vs_lat_or_lon_plot(self, datasets):
+        """Create the Hovmoeller plot with time vs latitude or longitude."""
+        plot_type = 'hovmoeller_time_vs_lat_or_lon'
+        if plot_type not in self.plots:
+            return
+
+        if not datasets:
+            raise ValueError(f"No input data to plot '{plot_type}' given")
+
+        # Get reference dataset if possible
+        ref_dataset = self._get_reference_dataset(datasets)
+        if ref_dataset is None:
+            logger.info("Plotting %s without reference dataset", plot_type)
+        else:
+            logger.info("Plotting %s with reference dataset '%s'", plot_type,
+                        self._get_label(ref_dataset))
+
+        # Get plot function
+        plot_func = self._get_plot_func(plot_type)
+
+        # Create a single plot for each dataset (incl. reference dataset if
+        # given)
+        for dataset in datasets:
+            if dataset == ref_dataset:
+                continue
+            ancestors = [dataset['filename']]
+            if ref_dataset is None:
+                (plot_path, netcdf_paths) = (
+                    self._plot_hovmoeller_time_vs_lat_or_lon_without_ref(
+                        plot_func,
+                        dataset)
+                )
+                caption = (
+                    f"Hovmoeller plot of {dataset['long_name']} of dataset "
+                    f"{dataset['dataset']} (project {dataset['project']}) "
+                    f"from {dataset['start_year']} to {dataset['end_year']}."
+                )
+            else:
+                (plot_path, netcdf_paths) = (
+                    self._plot_hovmoeller_time_vs_lat_or_lon_with_ref(
+                        plot_func, dataset, ref_dataset)
+                )
+                caption = (
+                    f"Hovmoeller plot of {dataset['long_name']} of dataset "
+                    f"{dataset['dataset']} (project {dataset['project']}) "
+                    f"including bias relative to {ref_dataset['dataset']} "
+                    f"(project {ref_dataset['project']}) from "
+                    f"{dataset['start_year']} to {dataset['end_year']}."
+                )
+                ancestors.append(ref_dataset['filename'])
+
+            # Save plot
+            plt.savefig(plot_path, **self.cfg['savefig_kwargs'])
+            logger.info("Wrote %s", plot_path)
+            plt.close()
+
+            # Save netCDFs
+            for (netcdf_path, cube) in netcdf_paths.items():
+                io.iris_save(cube, netcdf_path)
+
+            # Provenance tracking
+            provenance_record = {
+                'ancestors': ancestors,
+                'authors': ['schlund_manuel', 'kraft_jeremy', 'ruhe_lukas'],
+                'caption': caption,
+                'plot_types': ['zonal'],
+                'long_names': [dataset['long_name']],
+            }
+            with ProvenanceLogger(self.cfg) as provenance_logger:
+                provenance_logger.log(plot_path, provenance_record)
+                for netcdf_path in netcdf_paths:
+                    provenance_logger.log(netcdf_path, provenance_record)
+
     def compute(self):
         """Plot preprocessed data."""
-        for (short_name, datasets) in self.grouped_input_data.items():
-            logger.info("Processing variable %s", short_name)
-            self.create_timeseries_plot(datasets, short_name)
-            self.create_annual_cycle_plot(datasets, short_name)
-            self.create_map_plot(datasets, short_name)
-            self.create_zonal_mean_profile_plot(datasets, short_name)
-            self.create_1d_profile_plot(datasets, short_name)
+        for (var_key, datasets) in self.grouped_input_data.items():
+            logger.info("Processing variable %s", var_key)
+            self.create_timeseries_plot(datasets)
+            self.create_annual_cycle_plot(datasets)
+            self.create_map_plot(datasets)
+            self.create_zonal_mean_profile_plot(datasets)
+            self.create_1d_profile_plot(datasets)
+            self.create_variable_vs_lat_plot(datasets)
+            self.create_hovmoeller_z_vs_time_plot(datasets)
+            self.create_hovmoeller_time_vs_lat_or_lon_plot(datasets)
 
 
 def main():
diff --git a/esmvaltool/recipes/monitor/recipe_monitor_with_refs.yml b/esmvaltool/recipes/monitor/recipe_monitor_with_refs.yml
index 0d1415979a..e75cc763da 100644
--- a/esmvaltool/recipes/monitor/recipe_monitor_with_refs.yml
+++ b/esmvaltool/recipes/monitor/recipe_monitor_with_refs.yml
@@ -7,6 +7,11 @@ documentation:
     (ongoing) model simulations.
   authors:
     - schlund_manuel
+    - heuer_helge
+    - kraft_jeremy
+    - kuehbacher_birgit
+    - ruhe_lukas
+    - sarauer_ellen
     - winterstein_franziska
   maintainer:
     - schlund_manuel
@@ -14,8 +19,8 @@ documentation:
 
 datasets:
   # Note: plot_label currently only used by diagnostic plot_multiple_annual_cycles
-  - {project: CMIP6, dataset: EC-Earth3, exp: historical, ensemble: r1i1p1f1, grid: gr, plot_label: 'EC-Earth3 historical'}
-  - {project: CMIP6, dataset: CanESM5,   exp: historical, ensemble: r1i1p1f1, grid: gn, plot_label: 'Reference (CanESM5 historical)', reference_for_monitor_diags: true}
+  - {project: CMIP6, dataset: MPI-ESM1-2-HR, exp: historical, ensemble: r1i1p1f1, grid: gn, plot_label: 'MPI-ESM1-2-HR historical'}
+  - {project: CMIP6, dataset: MPI-ESM1-2-LR, exp: historical, ensemble: r1i1p1f1, grid: gn, plot_label: 'Reference (MPI-ESM1-2-LR historical)', reference_for_monitor_diags: true}
 
 
 preprocessors:
@@ -67,6 +72,36 @@ preprocessors:
       scheme: linear
       coordinate: air_pressure
 
+  var_vs_lat:
+    climate_statistics:
+      operator: mean
+    regrid:
+      target_grid: 2x2
+      scheme: linear
+    zonal_statistics:
+      operator: mean
+    convert_units:
+      units: mm day-1
+
+  global_mean_extract_levels:
+    custom_order: true
+    extract_levels:
+      levels: {cmor_table: CMIP6, coordinate: alt16}
+      scheme: linear
+      coordinate: altitude
+    regrid:
+      target_grid: 2x2
+      scheme: linear
+    area_statistics:
+      operator: mean
+
+  zonal_mean_2d:
+    regrid:
+      target_grid: 2x2
+      scheme: linear
+    zonal_statistics:
+      operator: mean
+
 
 diagnostics:
 
@@ -87,9 +122,9 @@ diagnostics:
             annual_mean_kwargs:
               linestyle: '--'
             plot_kwargs:
-              EC-Earth3:  # = dataset since 'facet_used_for_labels' is 'dataset' by default
+              MPI-ESM1-2-HR:  # = dataset since 'facet_used_for_labels' is 'dataset' by default
                 color: C0
-              CanESM5:
+              MPI-ESM1-2-LR:
                 color: black
 
   plot_multiple_annual_cycles:
@@ -108,9 +143,9 @@ diagnostics:
             legend_kwargs:
               loc: upper right
             plot_kwargs:
-              'EC-Earth3 historical':  # = plot_label since 'facet_used_for_labels: plot_label'
+              'MPI-ESM1-2-HR historical':  # = plot_label since 'facet_used_for_labels: plot_label'
                 color: C0
-              'Reference (CanESM5 historical)':
+              'Reference (MPI-ESM1-2-LR historical)':
                 color: black
             pyplot_kwargs:
               title: Near-Surface Air Temperature on Northern Hemisphere
@@ -150,6 +185,7 @@ diagnostics:
             plot_kwargs_bias:
               levels: [-10.0, -7.5, -5.0, -2.5, 0.0, 2.5, 5.0, 7.5, 10.0]
 
+
   plot_1D_profiles_with_references:
     description: Plot 1D profiles including reference datasets.
     variables:
@@ -164,7 +200,58 @@ diagnostics:
         plots:
           1d_profile:
             plot_kwargs:
-              EC-Earth3:  # = dataset since 'facet_used_for_labels' is 'dataset' by default
+              MPI-ESM1-2-HR:  # = dataset since 'facet_used_for_labels' is 'dataset' by default
                 color: C0
-              CanESM5:
+              MPI-ESM1-2-LR:
                 color: black
+
+  plot_variable_vs_latitude:
+    description: Creates a single-panel variable plot over latitude.
+    variables:
+      pr:
+        preprocessor: var_vs_lat
+        mip: Amon
+        timerange: '20000101/20030101'
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        script: monitor/multi_datasets.py
+        plots:
+          variable_vs_lat:
+
+  plot_hovmoeller_z_vs_time:
+    description: Plot Hovmoeller Z vs. time including reference datasets.
+    variables:
+      ta:
+        preprocessor: global_mean_extract_levels
+        mip: Amon
+        timerange: '2000/2004'
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        script: monitor/multi_datasets.py
+        plots:
+          hovmoeller_z_vs_time:
+            plot_func: contourf
+            common_cbar: true
+            time_format: '%Y'
+            log_y: false
+            pyplot_kwargs:
+              ylim: [0, 20000]
+
+  plot_time_vs_lat_with_references:
+    description: Plot Hovmoeller time vs. latitude including reference datasets.
+    variables:
+      tas:
+        mip: Amon
+        preprocessor: zonal_mean_2d
+        timerange: '2000/2004'
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        script: monitor/multi_datasets.py
+        plots:
+          hovmoeller_time_vs_lat_or_lon:
+            common_cbar: true
+            show_x_minor_ticks: false
+            time_format: '%Y'