diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index 92a327a6e1b3bf0da5314546cd83da80e87be6cf..43f1864f7354c1f711bb886f4f97eda56439ab89 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -631,17 +631,16 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass):  # pragma: no cover
         self._boot_dim = boot_dim
         self._boot_type = self._set_bootstrap_type(bootstrap_type)
         self._boot_method = self._set_bootstrap_method(bootstrap_method)
+        self._number_of_bootstraps = 0
         self._branches_names = branch_names
         self._ylim = ylim
 
-        title_d = {"single input": "Single Inputs", "branch": "Input Branches", "variable": "Variables"}
-        self._title = f"{model_name}\nImportance of {title_d[self._boot_type]}"
         self._data = self._prepare_data(data, sampling)
+        self._set_title(model_name)
         if "branch" in self._data.columns:
             plot_name = self.plot_name
             for branch in self._data["branch"].unique():
-                branch_name = self._branches_names[branch] if self._branches_names is not None else branch
-                self._title = f"{model_name}\nImportance of {title_d[self._boot_type]} ({branch_name})"
+                self._set_title(model_name, branch)
                 self._plot(branch=branch)
                 self.plot_name = f"{plot_name}_{branch}"
                 self._save()
@@ -661,6 +660,21 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass):  # pragma: no cover
     def _set_bootstrap_type(boot_type):
         return {"singleinput": "single input"}.get(boot_type, boot_type)
 
+    def _set_title(self, model_name, branch=None):
+        title_d = {"single input": "Single Inputs", "branch": "Input Branches", "variable": "Variables"}
+        base_title = f"{model_name}\nImportance of {title_d[self._boot_type]}"
+
+        additional = []
+        if branch is not None:
+            branch_name = self._branches_names[branch] if self._branches_names is not None else branch
+            additional.append(branch_name)
+        if self._number_of_bootstraps > 1:
+            additional.append(f"n={self._number_of_bootstraps}")
+        additional_title = ", ".join(additional)
+        if len(additional_title) > 0:
+            additional_title = f" ({additional_title})"
+        self._title = base_title + additional_title
+
     @staticmethod
     def _set_bootstrap_method(boot_method):
         return {"zero_mean": "zero mean", "shuffle": "shuffled"}.get(boot_method, boot_method)
@@ -698,6 +712,7 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass):  # pragma: no cover
         self._labels = [str(i) + sampling_letter for i in data.coords[self._ahead_dim].values]
         if station_dim not in data.dims:
             data = data.expand_dims(station_dim)
+        self._number_of_bootstraps = np.unique(data.coords[self._boot_dim].values).shape[0]
         return data.to_dataframe("data").reset_index(level=np.arange(len(data.dims)).tolist())
 
     @staticmethod
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index 64ac980521361443283cab1532ba05ced13cb484..e3aa2154559622fdd699d430bc4d386499f5114d 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -272,7 +272,8 @@ class PostProcessing(RunEnvironment):
         if _iter == 0:
             self.feature_importance_skill_scores = {}
         for boot_type in to_list(bootstrap_type):
-            self.feature_importance_skill_scores[boot_type] = {}
+            if _iter == 0:
+                self.feature_importance_skill_scores[boot_type] = {}
             for boot_method in to_list(bootstrap_method):
                 try:
                     if create_new_bootstraps:
@@ -281,7 +282,7 @@ class PostProcessing(RunEnvironment):
                     boot_skill_score = self.calculate_feature_importance_skill_scores(bootstrap_type=boot_type,
                                                                                       bootstrap_method=boot_method)
                     self.feature_importance_skill_scores[boot_type][boot_method] = boot_skill_score
-                except FileNotFoundError:
+                except (FileNotFoundError, ValueError):
                     if _iter != 0:
                         raise RuntimeError(f"calculate_feature_importance ({boot_type}, {boot_type}) was called for the "
                                            f"2nd time. This means, that something internally goes wrong. Please check "
@@ -387,8 +388,9 @@ class PostProcessing(RunEnvironment):
                             skill_scores.general_skill_score(data, forecast_name=boot_var,
                                                              reference_name=reference_name, dim=self.index_dim))
                     tmp = xr.DataArray(np.expand_dims(np.array(boot_scores), axis=-1),
-                                       coords=[range(1, self.window_lead_time + 1), range(number_of_bootstraps),
-                                               [boot_var]],
+                                       coords={self.ahead_dim: range(1, self.window_lead_time + 1),
+                                               self.uncertainty_estimate_boot_dim: range(number_of_bootstraps),
+                                               self.boot_var_dim: [boot_var]},
                                        dims=[self.ahead_dim, self.uncertainty_estimate_boot_dim, self.boot_var_dim])
                     skill.append(tmp)
 
@@ -585,7 +587,7 @@ class PostProcessing(RunEnvironment):
 
         try:
             if "PlotSampleUncertaintyFromBootstrap" in plot_list and self.uncertainty_estimate is not None:
-                block_length= self.data_store.get_default("block_length", default="1m", scope="uncertainty_estimate")
+                block_length = self.data_store.get_default("block_length", default="1m", scope="uncertainty_estimate")
                 PlotSampleUncertaintyFromBootstrap(
                     data=self.uncertainty_estimate, plot_folder=self.plot_path, model_type_dim=self.model_type_dim,
                     dim_name_boots=self.uncertainty_estimate_boot_dim, error_measure="mean squared error",