diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index 10cde645eed44e440f5687d6b5498b5add3ea98d..00c925031e8e8bc804979c75052355837f5cb614 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -698,12 +698,14 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
 
     """
 
-    def __init__(self, data: pd.DataFrame, plot_folder=".", model_setup="NN", sampling="daily"):
+    def __init__(self, data: pd.DataFrame, plot_folder=".", model_setup="NN", sampling="daily",
+                 model_name_for_plots=None):
         """Initialise."""
         super().__init__(plot_folder, f"skill_score_competitive_{model_setup}")
         self._model_setup = model_setup
         self._sampling = self._get_sampling(sampling)
         self._labels = None
+        self._model_name_for_plots = model_name_for_plots
         self._data = self._prepare_data(data)
         default_plot_name = self.plot_name
         # draw full detail plot
@@ -745,6 +747,8 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
         size = max([len(np.unique(self._data.comparison)), 6])
         fig, ax = plt.subplots(figsize=(size, size * 0.8))
         data = self._filter_comparisons(self._data) if single_model_comparison is True else self._data
+        if self._model_name_for_plots is not None:
+            data['comparison'] = [i.replace('nn-', f'{self._model_name_for_plots}-') for i in data['comparison']]
         order = self._create_pseudo_order(data)
         sns.boxplot(x="comparison", y="data", hue="ahead", data=data, whis=1., ax=ax, palette="Blues_d",
                     showmeans=True, meanprops={"markersize": 3, "markeredgecolor": "k"}, flierprops={"marker": "."},
@@ -761,6 +765,8 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
         """Plot skill scores of the comparisons, but vertically aligned."""
         fig, ax = plt.subplots()
         data = self._filter_comparisons(self._data) if single_model_comparison is True else self._data
+        if self._model_name_for_plots is not None:
+            data['comparison'] = [i.replace('nn-', f'{self._model_name_for_plots}-') for i in data['comparison']]
         order = self._create_pseudo_order(data)
         sns.boxplot(y="comparison", x="data", hue="ahead", data=data, whis=1., ax=ax, palette="Blues_d",
                     showmeans=True, meanprops={"markersize": 3, "markeredgecolor": "k"}, flierprops={"marker": "."},
@@ -780,7 +786,8 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
         return uniq[index.argsort()]
 
     def _filter_comparisons(self, data):
-        filtered_headers = list(filter(lambda x: "nn-" in x, data.comparison.unique()))
+        # filtered_headers = list(filter(lambda x: "nn-" in x, data.comparison.unique()))
+        filtered_headers = list(filter(lambda x: f"{self._model_name_for_plots}-" in x, data.comparison.unique()))
         return data[data.comparison.isin(filtered_headers)]
 
     def _lim(self) -> Tuple[float, float]:
@@ -906,14 +913,19 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
         data_second = self._select_data(df=data, variables=remaining_vars, column_name='boot_var')
 
         order_first = self.set_order_for_x_axis(separate_vars)
-        order_second = self.set_order_for_x_axis(remaining_vars)
 
         order_second, center_names_second = self.set_order_for_x_axis(remaining_vars, return_center_names=True)
         number_of_vars_second = len(order_second)
         group_size = int(number_of_vars_second / len(center_names_second))
 
+        if len(self._individual_vars) > 20:
+            figsize = (len(self._individual_vars) / 2, 10)
+        else:
+            figsize = (15, 10)
+
+
         fig, ax = plt.subplots(nrows=1, ncols=2,
-                               figsize=(len(self._individual_vars) / 2, 10),
+                               figsize=figsize,
                                gridspec_kw={'width_ratios': [len(separate_vars),
                                                              len(remaining_vars)
                                                              ]
@@ -1004,7 +1016,7 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
         if number_of_vars > 20:
             fig, ax = plt.subplots(figsize=(number_of_vars/2, 10))
         else:
-            fig, ax = plt.subplots()
+            fig, ax = plt.subplots(figsize=(15, 10))
         sns.boxplot(x=self._x_name, y="data", hue="ahead", data=self._data, ax=ax, whis=1., palette="Blues_d",
                     showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"}, flierprops={"marker": "."},
                     order=order)
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index 0d80fe7431347c48134ed1a300b93cf8d3e33195..d5033cf70a4a7d13c253843ae627360c0b2596d4 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -84,6 +84,7 @@ class PostProcessing(RunEnvironment):
         self.competitor_path = self.data_store.get("competitor_path")
         self.competitors = to_list(self.data_store.get_default("competitors", default=[]))
         self.forecast_indicator = "nn"
+        self.model_name_for_plots = self.data_store.get_default("model_name_for_plots", default=None)
         self._run()
 
     def _run(self):
@@ -363,7 +364,8 @@ class PostProcessing(RunEnvironment):
         try:
             if "PlotCompetitiveSkillScore" in plot_list:
                 PlotCompetitiveSkillScore(self.skill_scores[0], plot_folder=self.plot_path,
-                                          model_setup=self.forecast_indicator, sampling=self._sampling)
+                                          model_setup=self.forecast_indicator, sampling=self._sampling,
+                                          model_name_for_plots=self.model_name_for_plots)
         except Exception as e:
             logging.error(f"Could not create plot PlotCompetitiveSkillScore due to the following error: {e}")