diff --git a/docs/source/api_reference/regression.rst b/docs/source/api_reference/regression.rst
index 927f148d1..2855e6b9d 100644
--- a/docs/source/api_reference/regression.rst
+++ b/docs/source/api_reference/regression.rst
@@ -44,6 +44,18 @@ Model selection and tuning
 
     evaluate
 
+Online learning
+---------------
+
+.. currentmodule:: skpro.regression.online
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    OnlineRefit
+    OnlineDontRefit
+
 Reduction - adding ``predict_proba``
 ------------------------------------
 
diff --git a/skpro/registry/_tags.py b/skpro/registry/_tags.py
index 53fd2f4e0..d35470ca0 100644
--- a/skpro/registry/_tags.py
+++ b/skpro/registry/_tags.py
@@ -122,6 +122,12 @@
         "bool",
         "whether estimator supports missing values",
     ),
+    (
+        "capability:online",
+        "regressor_proba",
+        "bool",
+        "whether estimator supports online updates via update",
+    ),
     (
         "X_inner_mtype",
         "regressor_proba",
diff --git a/skpro/regression/base/_base.py b/skpro/regression/base/_base.py
index bbd205ad8..91def4db3 100644
--- a/skpro/regression/base/_base.py
+++ b/skpro/regression/base/_base.py
@@ -33,6 +33,7 @@ class BaseProbaRegressor(BaseEstimator):
         "capability:survival": False,
         "capability:multioutput": False,
         "capability:missing": True,
+        "capability:online": False,
         "X_inner_mtype": "pd_DataFrame_Table",
         "y_inner_mtype": "pd_DataFrame_Table",
         "C_inner_mtype": "pd_DataFrame_Table",
@@ -136,6 +137,74 @@ def _fit(self, X, y, C=None):
         """
         raise NotImplementedError
 
+    def update(self, X, y, C=None):
+        """Update regressor with a new batch of training data.
+
+        Only estimators with the ``capability:online`` tag (value ``True``)
+        provide this method, otherwise the method ignores the call and
+        discards the data passed.
+
+        State required:
+            Requires state to be "fitted".
+
+        Writes to self:
+            Updates fitted model attributes ending in "_".
+
+        Parameters
+        ----------
+        X : pandas DataFrame
+            feature instances to fit regressor to
+        y : pd.DataFrame, must be same length as X
+            labels to fit regressor to
+        C : ignored, optional (default=None)
+            censoring information for survival analysis
+            All probabilistic regressors assume data to be uncensored
+
+        Returns
+        -------
+        self : reference to self
+        """
+        capa_online = self.get_tag("capability:online")
+        capa_surv = self.get_tag("capability:survival")
+
+        if not capa_online:
+            return self
+
+        check_ret = self._check_X_y(X, y, C, return_metadata=True)
+
+        # get inner X, y, C
+        X_inner = check_ret["X_inner"]
+        y_inner = check_ret["y_inner"]
+        if capa_surv:
+            C_inner = check_ret["C_inner"]
+
+        if not capa_surv:
+            return self._update(X_inner, y_inner)
+        else:
+            return self._update(X_inner, y_inner, C=C_inner)
+
+    def _update(self, X, y, C=None):
+        """Update regressor with a new batch of training data.
+
+        State required:
+            Requires state to be "fitted".
+
+        Writes to self:
+            Updates fitted model attributes ending in "_".
+
+        Parameters
+        ----------
+        X : pandas DataFrame
+            feature instances to fit regressor to
+        y : pandas DataFrame, must be same length as X
+            labels to fit regressor to
+
+        Returns
+        -------
+        self : reference to self
+        """
+        raise NotImplementedError
+
     def predict(self, X):
         """Predict labels for data from features.
 
diff --git a/skpro/regression/base/_delegate.py b/skpro/regression/base/_delegate.py
index 52d31ab1d..2cd581fc2 100644
--- a/skpro/regression/base/_delegate.py
+++ b/skpro/regression/base/_delegate.py
@@ -67,6 +67,38 @@ def _fit(self, X, y, C=None):
         estimator.fit(X=X, y=y, C=C)
         return self
 
+    def _update(self, X, y, C=None):
+        """Update regressor with a new batch of training data.
+
+        State required:
+            Requires state to be "fitted" = self.is_fitted=True
+
+        Writes to self:
+            Updates fitted model attributes ending in "_".
+
+        Parameters
+        ----------
+        X : pandas DataFrame
+            feature instances to fit regressor to
+        y : pd.DataFrame, must be same length as X
+            labels to fit regressor to
+        C : pd.DataFrame, optional (default=None)
+            censoring information for survival analysis,
+            should have same column name as y, same length as X and y
+            should have entries 0 and 1 (float or int)
+            0 = uncensored, 1 = (right) censored
+            if None, all observations are assumed to be uncensored
+            Can be passed to any probabilistic regressor,
+            but is ignored if capability:survival tag is False.
+
+        Returns
+        -------
+        self : reference to self
+        """
+        estimator = self._get_delegate()
+        estimator.update(X=X, y=y, C=C)
+        return self
+
     def _predict(self, X):
         """Predict labels for data from features.
 
diff --git a/skpro/regression/compose/_pipeline.py b/skpro/regression/compose/_pipeline.py
index 85466d876..c06be454d 100644
--- a/skpro/regression/compose/_pipeline.py
+++ b/skpro/regression/compose/_pipeline.py
@@ -336,7 +336,11 @@ def __init__(self, steps):
 
         super().__init__()
 
-        tags_to_clone = ["capability:multioutput", "capability:survival"]
+        tags_to_clone = [
+            "capability:multioutput",
+            "capability:survival",
+            "capability:online",
+        ]
         self.clone_tags(self.regressor_, tags_to_clone)
 
     @property
@@ -427,6 +431,38 @@ def _fit(self, X, y, C=None):
 
         return self
 
+    def _update(self, X, y, C=None):
+        """Update regressor with a new batch of training data.
+
+        State required:
+            Requires state to be "fitted" = self.is_fitted=True
+
+        Writes to self:
+            Updates fitted model attributes ending in "_".
+
+        Parameters
+        ----------
+        X : pandas DataFrame
+            feature instances to fit regressor to
+        y : pd.DataFrame, must be same length as X
+            labels to fit regressor to
+        C : pd.DataFrame, optional (default=None)
+            censoring information for survival analysis,
+            should have same column name as y, same length as X and y
+            should have entries 0 and 1 (float or int)
+            0 = uncensored, 1 = (right) censored
+            if None, all observations are assumed to be uncensored
+            Can be passed to any probabilistic regressor,
+            but is ignored if capability:survival tag is False.
+
+        Returns
+        -------
+        self : reference to self
+        """
+        X = self._transform(X)
+        self.regressor_.update(X=X, y=y, C=C)
+        return self
+
     def _predict(self, X):
         """Predict labels for data from features.
 
diff --git a/skpro/regression/online/__init__.py b/skpro/regression/online/__init__.py
new file mode 100644
index 000000000..90d20a346
--- /dev/null
+++ b/skpro/regression/online/__init__.py
@@ -0,0 +1,7 @@
+"""Meta-algorithms to build online regression models."""
+# copyright: skpro developers, BSD-3-Clause License (see LICENSE file)
+
+from skpro.regression.online._dont_refit import OnlineDontRefit
+from skpro.regression.online._refit import OnlineRefit
+
+__all__ = ["OnlineDontRefit", "OnlineRefit"]
diff --git a/skpro/regression/online/_dont_refit.py b/skpro/regression/online/_dont_refit.py
new file mode 100644
index 000000000..6ae70c4fd
--- /dev/null
+++ b/skpro/regression/online/_dont_refit.py
@@ -0,0 +1,106 @@
+"""Meta-strategy for online learning: turn off online update."""
+
+__author__ = ["fkiraly"]
+__all__ = ["OnlineDontRefit"]
+
+from skpro.regression.base import _DelegatedProbaRegressor
+
+
+class OnlineDontRefit(_DelegatedProbaRegressor):
+    """Simple online regression strategy, turns off any refitting.
+
+    In ``fit``, behaves like the wrapped regressor.
+    In ``update``, does nothing, overriding any other logic.
+
+    This strategy is useful when the wrapped regressor is already an online regressor,
+    to create a "no-op" online regressor for comparison.
+
+    Parameters
+    ----------
+    estimator : skpro regressor, descendant of BaseProbaRegressor
+        regressor to be update-refitted on all data, blueprint
+
+    Attributes
+    ----------
+    estimator_ : skpro regressor, descendant of BaseProbaRegressor
+        clone of the regressor passed in the constructor, fitted on all data
+    """
+
+    _tags = {"capability:online": False}
+
+    def __init__(self, estimator):
+        self.estimator = estimator
+
+        super().__init__()
+
+        tags_to_clone = [
+            "capability:missing",
+            "capability:survival",
+        ]
+        self.clone_tags(estimator, tags_to_clone)
+
+        self.estimator_ = self.estimator.clone()
+
+    def _update(self, X, y, C=None):
+        """Update regressor with new batch of training data.
+
+        State required:
+            Requires state to be "fitted".
+
+        Writes to self:
+            Updates fitted model attributes ending in "_".
+
+        Parameters
+        ----------
+        X : pandas DataFrame
+            feature instances to fit regressor to
+        y : pandas DataFrame, must be same length as X
+            labels to fit regressor to
+        C : pd.DataFrame, optional (default=None)
+            censoring information for survival analysis,
+            should have same column name as y, same length as X and y
+            should have entries 0 and 1 (float or int)
+            0 = uncensored, 1 = (right) censored
+            if None, all observations are assumed to be uncensored
+            Can be passed to any probabilistic regressor,
+            but is ignored if capability:survival tag is False.
+
+        Returns
+        -------
+        self : reference to self
+        """
+        return self
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator.
+
+        Parameters
+        ----------
+        parameter_set : str, default="default"
+            Name of the set of test parameters to return, for use in tests. If no
+            special parameters are defined for a value, will return `"default"` set.
+
+        Returns
+        -------
+        params : dict or list of dict, default = {}
+            Parameters to create testing instances of the class
+            Each dict are parameters to construct an "interesting" test instance, i.e.,
+            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
+            `create_test_instance` uses the first (or only) dictionary in `params`
+        """
+        from sklearn.linear_model import LinearRegression
+
+        from skpro.regression.residual import ResidualDouble
+        from skpro.survival.coxph import CoxPH
+        from skpro.utils.validation._dependencies import _check_estimator_deps
+
+        regressor = ResidualDouble(LinearRegression())
+
+        params = [{"estimator": regressor}]
+
+        if _check_estimator_deps(CoxPH, severity="none"):
+            coxph = CoxPH()
+            params.append({"estimator": coxph})
+
+        return params
diff --git a/skpro/regression/online/_refit.py b/skpro/regression/online/_refit.py
new file mode 100644
index 000000000..8685ec17e
--- /dev/null
+++ b/skpro/regression/online/_refit.py
@@ -0,0 +1,181 @@
+"""Meta-strategy for online learning: refit on full data."""
+
+__author__ = ["fkiraly"]
+__all__ = ["OnlineRefit"]
+
+import pandas as pd
+
+from skpro.regression.base import _DelegatedProbaRegressor
+
+
+class OnlineRefit(_DelegatedProbaRegressor):
+    """Simple online regression strategy, by refitting the regressor on all data.
+
+    In ``fit`` and ``update``, remembers all data.
+    In ``update``, refits the regressor on all data seen so far.
+
+    Caveat: data indices are reset to RangeIndex internally, even if some indices
+    passed in ``fit`` and ``update`` overlap.
+
+    Parameters
+    ----------
+    estimator : skpro regressor, descendant of BaseProbaRegressor
+        regressor to be update-refitted on all data, blueprint
+
+    Attributes
+    ----------
+    estimator_ : skpro regressor, descendant of BaseProbaRegressor
+        clone of the regressor passed in the constructor, fitted on all data
+    """
+
+    _tags = {"capability:online": True}
+
+    def __init__(self, estimator):
+        self.estimator = estimator
+
+        super().__init__()
+
+        tags_to_clone = [
+            "capability:missing",
+            "capability:survival",
+        ]
+        self.clone_tags(estimator, tags_to_clone)
+
+    def _fit(self, X, y, C=None):
+        """Fit regressor to training data.
+
+        Writes to self:
+            Sets fitted model attributes ending in "_".
+
+        Parameters
+        ----------
+        X : pandas DataFrame
+            feature instances to fit regressor to
+        y : pandas DataFrame, must be same length as X
+            labels to fit regressor to
+        C : pd.DataFrame, optional (default=None)
+            censoring information for survival analysis,
+            should have same column name as y, same length as X and y
+            should have entries 0 and 1 (float or int)
+            0 = uncensored, 1 = (right) censored
+            if None, all observations are assumed to be uncensored
+            Can be passed to any probabilistic regressor,
+            but is ignored if capability:survival tag is False.
+
+        Returns
+        -------
+        self : reference to self
+        """
+        estimator = self.estimator.clone()
+
+        estimator.fit(X=X, y=y, C=C)
+        self.estimator_ = estimator
+
+        # remember data
+        self._X = X
+        self._y = y
+        self._C = C
+
+        return self
+
+    def _update(self, X, y, C=None):
+        """Update regressor with new batch of training data.
+
+        State required:
+            Requires state to be "fitted".
+
+        Writes to self:
+            Updates fitted model attributes ending in "_".
+
+        Parameters
+        ----------
+        X : pandas DataFrame
+            feature instances to fit regressor to
+        y : pandas DataFrame, must be same length as X
+            labels to fit regressor to
+        C : pd.DataFrame, optional (default=None)
+            censoring information for survival analysis,
+            should have same column name as y, same length as X and y
+            should have entries 0 and 1 (float or int)
+            0 = uncensored, 1 = (right) censored
+            if None, all observations are assumed to be uncensored
+            Can be passed to any probabilistic regressor,
+            but is ignored if capability:survival tag is False.
+
+        Returns
+        -------
+        self : reference to self
+        """
+        X_pool = self._update_data(self._X, X)
+        y_pool = self._update_data(self._y, y)
+        C_pool = self._update_data(self._C, C)
+
+        estimator = self.estimator.clone()
+        estimator.fit(X=X_pool, y=y_pool, C=C_pool)
+        self.estimator_ = estimator
+
+        # remember data
+        self._X = X_pool
+        self._y = y_pool
+        self._C = C_pool
+
+        return self
+
+    def _update_data(self, X, X_new):
+        """Update data with new batch of training data.
+
+        Treats X_new as data with new indices, even if some indices overlap with X.
+
+        Parameters
+        ----------
+        X : pandas DataFrame
+        X_new : pandas DataFrame
+
+        Returns
+        -------
+        X_updated : pandas DataFrame
+            concatenated data, with reset index
+        """
+        if X is None and X_new is None:
+            return None
+        if X is None and X_new is not None:
+            return X_new.reset_index(drop=True)
+        if X is not None and X_new is None:
+            return X.reset_index(drop=True)
+        # else, both X and X_new are not None
+        X_updated = pd.concat([X, X_new], ignore_index=True)
+        return X_updated
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator.
+
+        Parameters
+        ----------
+        parameter_set : str, default="default"
+            Name of the set of test parameters to return, for use in tests. If no
+            special parameters are defined for a value, will return `"default"` set.
+
+        Returns
+        -------
+        params : dict or list of dict, default = {}
+            Parameters to create testing instances of the class
+            Each dict are parameters to construct an "interesting" test instance, i.e.,
+            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
+            `create_test_instance` uses the first (or only) dictionary in `params`
+        """
+        from sklearn.linear_model import LinearRegression
+
+        from skpro.regression.residual import ResidualDouble
+        from skpro.survival.coxph import CoxPH
+        from skpro.utils.validation._dependencies import _check_estimator_deps
+
+        regressor = ResidualDouble(LinearRegression())
+
+        params = [{"estimator": regressor}]
+
+        if _check_estimator_deps(CoxPH, severity="none"):
+            coxph = CoxPH()
+            params.append({"estimator": coxph})
+
+        return params
diff --git a/skpro/regression/tests/test_all_regressors.py b/skpro/regression/tests/test_all_regressors.py
index 06d991be3..e39eb33ef 100644
--- a/skpro/regression/tests/test_all_regressors.py
+++ b/skpro/regression/tests/test_all_regressors.py
@@ -166,3 +166,40 @@ def test_pred_quantiles_interval(self, object_instance, alpha):
         # check predict_quantiles output contract
         pred_q = regressor.predict_quantiles(X_test, alpha)
         self._check_predict_quantiles(pred_q, X_test, y_train, alpha)
+
+    def test_online_update(self, object_instance):
+        """Test online update of regressor."""
+        import pandas as pd
+        from sklearn.datasets import load_diabetes
+        from sklearn.model_selection import train_test_split
+
+        X, y = load_diabetes(return_X_y=True, as_frame=True)
+        X = X.iloc[:70]
+        y = y.iloc[:70]
+        y = pd.DataFrame(y)
+
+        X_train, X_test, y_train, _ = train_test_split(X, y)
+        X_fit, X_update, y_fit, y_update = train_test_split(X_train, y_train)
+        X_upd1, X_upd2, y_upd1, y_upd2 = train_test_split(X_update, y_update)
+
+        regressor = object_instance
+        regressor.fit(X_fit, y_fit)
+
+        regressor.update(X_upd1, y_upd1)
+        y_pred1 = regressor.predict(X_upd1)
+        y_pred2 = regressor.predict(X_upd2)
+
+        # check predict output contract
+        assert isinstance(y_pred2, pd.DataFrame)
+        assert (y_pred1.index == X_upd1.index).all()
+        assert (y_pred1.columns == y_fit.columns).all()
+        assert (y_pred2.index == X_upd2.index).all()
+        assert (y_pred2.columns == y_fit.columns).all()
+
+        regressor.update(X_upd2, y_upd2)
+        y_pred_test = regressor.predict(X_test)
+
+        # check predict output contract
+        assert isinstance(y_pred_test, pd.DataFrame)
+        assert (y_pred_test.index == X_test.index).all()
+        assert (y_pred_test.columns == y_fit.columns).all()
diff --git a/skpro/survival/base.py b/skpro/survival/base.py
index 403d43808..a083d08e1 100644
--- a/skpro/survival/base.py
+++ b/skpro/survival/base.py
@@ -47,3 +47,36 @@ def fit(self, X, y, C=None):
         """
         super().fit(X=X, y=y, C=C)
         return self
+
+    def update(self, X, y, C=None):
+        """Update regressor with a new batch of training data.
+
+        Only estimators with the ``capability:online`` tag (value ``True``)
+        provide this method, otherwise the method ignores the call and
+        discards the data passed.
+
+        State required:
+            Requires state to be "fitted".
+
+        Writes to self:
+            Updates fitted model attributes ending in "_".
+
+        Parameters
+        ----------
+        X : pandas DataFrame
+            feature instances to fit regressor to
+        y : pd.DataFrame, must be same length as X
+            labels to fit regressor to
+        C : pd.DataFrame, optional (default=None)
+            censoring information for survival analysis,
+            should have same column name as y, same length as X and y
+            should have entries 0 and 1 (float or int)
+            0 = uncensored, 1 = (right) censored
+            if None, all observations are assumed to be uncensored
+
+        Returns
+        -------
+        self : reference to self
+        """
+        super().update(X=X, y=y, C=C)
+        return self