diff --git a/bayesflow/approximators/continuous_approximator.py b/bayesflow/approximators/continuous_approximator.py
index 420b9a9a2..5a4f9702f 100644
--- a/bayesflow/approximators/continuous_approximator.py
+++ b/bayesflow/approximators/continuous_approximator.py
@@ -3,6 +3,7 @@
 import numpy as np
 
 import keras
+import warnings
 
 from bayesflow.adapters import Adapter
 from bayesflow.networks import InferenceNetwork, SummaryNetwork
@@ -539,7 +540,7 @@ def _sample(
             batch_shape, conditions=inference_conditions, **filter_kwargs(kwargs, self.inference_network.sample)
         )
 
-    def summaries(self, data: Mapping[str, np.ndarray], **kwargs) -> np.ndarray:
+    def summarize(self, data: Mapping[str, np.ndarray], **kwargs) -> np.ndarray:
         """
         Computes the learned summary statistics of given summary variables.
 
@@ -570,6 +571,14 @@ def summaries(self, data: Mapping[str, np.ndarray], **kwargs) -> np.ndarray:
 
         return summaries
 
+    def summaries(self, data: Mapping[str, np.ndarray], **kwargs) -> np.ndarray:
+        """
+        .. deprecated:: 2.0.4
+            `summaries` will be removed in version 2.0.5, it was renamed to `summarize` which should be used instead.
+        """
+        warnings.warn("`summaries` was renamed to `summarize` and will be removed in version 2.0.5.", FutureWarning)
+        return self.summarize(data=data, **kwargs)
+
     def log_prob(self, data: Mapping[str, np.ndarray], **kwargs) -> np.ndarray:
         """
         Computes the log-probability of given data under the model. The `data` dictionary is preprocessed using the
diff --git a/bayesflow/approximators/model_comparison_approximator.py b/bayesflow/approximators/model_comparison_approximator.py
index 4f23bcfcb..608158d2b 100644
--- a/bayesflow/approximators/model_comparison_approximator.py
+++ b/bayesflow/approximators/model_comparison_approximator.py
@@ -2,6 +2,7 @@
 
 import keras
 import numpy as np
+import warnings
 
 from bayesflow.adapters import Adapter
 from bayesflow.datasets import OnlineDataset
@@ -404,7 +405,7 @@ def predict(
 
         return keras.ops.convert_to_numpy(keras.ops.softmax(output) if probs else output)
 
-    def summaries(self, data: Mapping[str, np.ndarray], **kwargs) -> np.ndarray:
+    def summarize(self, data: Mapping[str, np.ndarray], **kwargs) -> np.ndarray:
         """
         Computes the learned summary statistics of given summary variables.
 
@@ -435,6 +436,14 @@ def summaries(self, data: Mapping[str, np.ndarray], **kwargs) -> np.ndarray:
 
         return summaries
 
+    def summaries(self, data: Mapping[str, np.ndarray], **kwargs) -> np.ndarray:
+        """
+        .. deprecated:: 2.0.4
+            `summaries` will be removed in version 2.0.5, it was renamed to `summarize` which should be used instead.
+        """
+        warnings.warn("`summaries` was renamed to `summarize` and will be removed in version 2.0.5.", FutureWarning)
+        return self.summarize(data=data, **kwargs)
+
     def _compute_logits(self, classifier_conditions: Tensor) -> Tensor:
         """Helper to compute projected logits from the classifier network."""
         logits = self.classifier_network(classifier_conditions)
diff --git a/bayesflow/diagnostics/metrics/model_misspecification.py b/bayesflow/diagnostics/metrics/model_misspecification.py
index c698d4eb2..802b2d336 100644
--- a/bayesflow/diagnostics/metrics/model_misspecification.py
+++ b/bayesflow/diagnostics/metrics/model_misspecification.py
@@ -142,8 +142,8 @@ def summary_space_comparison(
             "statistics, or want to compare raw data and not summary statistics, please use the "
             f"`bootstrap_comparison` function with `comparison_fn={comparison_fn_name}` on the respective arrays."
         )
-    observed_summaries = convert_to_numpy(approximator.summaries(observed_data))
-    reference_summaries = convert_to_numpy(approximator.summaries(reference_data))
+    observed_summaries = convert_to_numpy(approximator.summarize(observed_data))
+    reference_summaries = convert_to_numpy(approximator.summarize(reference_data))
 
     distance_observed, distance_null = bootstrap_comparison(
         observed_samples=observed_summaries,
diff --git a/tests/test_approximators/test_summaries.py b/tests/test_approximators/test_summarize.py
similarity index 82%
rename from tests/test_approximators/test_summaries.py
rename to tests/test_approximators/test_summarize.py
index 7962ddaab..04509cc3a 100644
--- a/tests/test_approximators/test_summaries.py
+++ b/tests/test_approximators/test_summarize.py
@@ -5,7 +5,7 @@
 
 def test_valid_summaries(approximator_with_summaries, mean_std_summary_network, monkeypatch):
     monkeypatch.setattr(approximator_with_summaries, "summary_network", mean_std_summary_network)
-    summaries = approximator_with_summaries.summaries({"summary_variables": keras.ops.ones((2, 3))})
+    summaries = approximator_with_summaries.summarize({"summary_variables": keras.ops.ones((2, 3))})
     assert_allclose(summaries, keras.ops.stack([keras.ops.ones((2,)), keras.ops.zeros((2,))], axis=-1))
 
 
@@ -13,11 +13,11 @@ def test_no_summary_network(approximator_with_summaries, monkeypatch):
     monkeypatch.setattr(approximator_with_summaries, "summary_network", None)
 
     with pytest.raises(ValueError):
-        approximator_with_summaries.summaries({"summary_variables": keras.ops.ones((2, 3))})
+        approximator_with_summaries.summarize({"summary_variables": keras.ops.ones((2, 3))})
 
 
 def test_no_summary_variables(approximator_with_summaries, mean_std_summary_network, monkeypatch):
     monkeypatch.setattr(approximator_with_summaries, "summary_network", mean_std_summary_network)
 
     with pytest.raises(ValueError):
-        approximator_with_summaries.summaries({})
+        approximator_with_summaries.summarize({})