diff --git a/darts/explainability/shap_explainer.py b/darts/explainability/shap_explainer.py
index 143ea0d8b9..26978bb00a 100644
--- a/darts/explainability/shap_explainer.py
+++ b/darts/explainability/shap_explainer.py
@@ -375,7 +375,7 @@ def summary_plot(
         num_samples: Optional[int] = None,
         plot_type: Optional[str] = "dot",
         **kwargs,
-    ):
+    ) -> Dict[int, Dict[str, shap.Explanation]]:
         """
         Display a shap plot summary for each horizon and each component dimension of the target.
         This method reuses the initial background data as foreground (potentially sampled) to give a general importance
@@ -395,6 +395,12 @@ def summary_plot(
             for the sake of performance.
         plot_type
             Optionally, specify which of the shap library plot type to use. Can be one of ``'dot', 'bar', 'violin'``.
+
+        Returns
+        -------
+        shaps_
+            A nested dictionary {horizon : {component : shap.Explaination}} containing the raw Explanations for all
+            the horizons and components.
         """
 
         horizons, target_components = self._process_horizons_and_targets(
@@ -421,6 +427,7 @@ def summary_plot(
                     plot_type=plot_type,
                     **kwargs,
                 )
+        return shaps_
 
     def force_plot_from_ts(
         self,
@@ -613,7 +620,7 @@ def __init__(
 
     def shap_explanations(
         self,
-        foreground_X,
+        foreground_X: pd.DataFrame,
         horizons: Optional[Sequence[int]] = None,
         target_components: Optional[Sequence[str]] = None,
     ) -> Dict[int, Dict[str, shap.Explanation]]:
@@ -735,8 +742,8 @@ def _create_regression_model_shap_X(
         target_series: Optional[Union[TimeSeries, Sequence[TimeSeries]]],
         past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]],
         future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]],
-        n_samples=None,
-        train=False,
+        n_samples: Optional[int] = None,
+        train: bool = False,
     ) -> pd.DataFrame:
         """
         Creates the shap format input for regression models.
diff --git a/darts/tests/explainability/test_shap_explainer.py b/darts/tests/explainability/test_shap_explainer.py
index a5e950adb4..dfc3773af2 100644
--- a/darts/tests/explainability/test_shap_explainer.py
+++ b/darts/tests/explainability/test_shap_explainer.py
@@ -596,6 +596,14 @@ def test_plot(self):
                 "power",
             )
 
+        # Check the dimensions of returned values
+        dict_shap_values = shap_explain.summary_plot(show=False)
+        # One nested dict per horizon
+        assert len(dict_shap_values) == m_0.output_chunk_length
+        # Size of nested dict match number of component
+        for i in range(1, m_0.output_chunk_length + 1):
+            assert len(dict_shap_values[i]) == self.target_ts.width
+
         # Wrong component name
         with pytest.raises(ValueError):
             shap_explain.summary_plot(horizons=[1], target_components=["test"])