diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index 425b26cfbde6a22b2dd61b87478f00c52ccda87e..0ff4afb37e76c0c03127bd84258ccb5fd2eb4a90 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -771,10 +771,20 @@ class PostProcessing(RunEnvironment):
                         *map(lambda x: external_data.sel(**{self.model_type_dim: x}),
                              [model_type, "obs"]), dim="index")
 
-            # skill score
+            # load competitors
             competitor = self.load_competitors(station)
             combined = self._combine_forecasts(external_data, competitor, dim=self.model_type_dim)
             model_list = remove_items(combined.coords[self.model_type_dim].values.tolist(), "obs") if combined is not None else None
+
+            # test errors of competitors
+            for model_type in remove_items(model_list, errors.keys()):
+                if model_type not in errors.keys():
+                    errors[model_type] = {}
+                errors[model_type][station] = statistics.calculate_error_metrics(
+                    *map(lambda x: combined.sel(**{self.model_type_dim: x}),
+                         [model_type, "obs"]), dim="index")
+
+            # skill score
             skill_score = statistics.SkillScores(combined, models=model_list, ahead_dim=self.ahead_dim)
             if external_data is not None:
                 skill_score_competitive[station] = skill_score.skill_scores()
@@ -828,7 +838,7 @@ class PostProcessing(RunEnvironment):
                     station_errors = {str(i.values): station_errors.sel(**{dim: i}) for i in sel_index}
                 for metric, vals in station_errors.items():
                     if metric == "n":
-                        continue
+                        metric = "count"
                     pd_vals = pd.DataFrame.from_dict({station: vals}).T
                     pd_vals.columns = [f"{metric}(t+{x})" for x in vals.coords["ahead"].values]
                     mc = metric_collection.get(metric, pd.DataFrame())