diff --git a/mlair/helpers/statistics.py b/mlair/helpers/statistics.py
index b63cbe801abd9f0d325a689a959bc494ad1b4485..90f4a8c95dcd8be8de6243ec58be0404f1cad073 100644
--- a/mlair/helpers/statistics.py
+++ b/mlair/helpers/statistics.py
@@ -301,7 +301,7 @@ class SkillScores:
                                                             observation_name=self.observation_name)
                                    for (first, second) in combinations]
         return skill_score
-    '''
+
     def climatological_skill_scores(self, internal_data: Data, forecast_name: str) -> xr.DataArray:
         """
         Calculate climatological skill scores according to Murphy (1988).
@@ -385,6 +385,7 @@ class SkillScores:
                     external_data=external_data).values.flatten())
 
         return skill_score
+    '''
 
     def _climatological_skill_score(self, internal_data, observation_name, forecast_name, mu_type=1,
                                     external_data=None):
diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index 6801addb7276d2638f44dd29fd7d521f6efa46a5..b7c0366e9cb9e165c63a13b567b89f220930b1fc 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -116,7 +116,16 @@ class PlotOversamplingContingency(AbstractPlotClass):
             except (FileNotFoundError, KeyError):
                 logging.debug(f"No competitor found for combination '{station_name}' and '{competitor_name}'.")
                 continue
-        return xr.concat(competing_predictions, "type") if len(competing_predictions) > 0 else None
+        if len(competing_predictions)==0:
+            return None
+        else:
+            comp_array = xr.concat(competing_predictions, "type")
+            if len(comp_array.coords[self.ahead_dim]) == self.window_lead_time:
+                return comp_array
+            else:
+                raise ValueError(f"Ahead dimensions of competitors do not match model."
+                                 f" Competitor ahead: {len(comp_array.coords[self.ahead_dim])}"
+                                 f" but window_lead_time is {self.window_lead_time}.")
 
     def _min_max_threshold(self):
         min_threshold = 0
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index 4defc4d6439b720d61ce4617ced178531a921f50..80d40a66245ca0310dbb20a7cee8655c2fc3b9ec 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -138,16 +138,7 @@ class PostProcessing(RunEnvironment):
             except (FileNotFoundError, KeyError):
                 logging.debug(f"No competitor found for combination '{station_name}' and '{competitor_name}'.")
                 continue
-        if len(competing_predictions)==0:
-            return None
-        else:
-            comp_array = xr.concat(competing_predictions, "type")
-            if len(comp_array.coords[self.ahead_dim]) == self.window_lead_time:
-                return comp_array
-            else:
-                raise ValueError(f"Ahead dimensions of competitors do not match model."
-                                 f" Competitor ahead: {len(comp_array.coords[self.ahead_dim])}"
-                                 f" but window_lead_time is {self.window_lead_time}.")
+        return xr.concat(competing_predictions, "type") if len(competing_predictions) > 0 else None
 
     def bootstrap_postprocessing(self, create_new_bootstraps: bool, _iter: int = 0, bootstrap_type="singleinput",
                                  bootstrap_method="shuffle") -> None: