diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index 1e3ce12638b6433fe7283769ac353d2e8579618e..8d7bfd0c37966b623f99ac72f316e36aa241dd3b 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -900,6 +900,9 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
         data_first = self._select_data(df=data, variables=separate_vars, column_name='boot_var')
         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)
+
         fig, ax = plt.subplots(nrows=1, ncols=2,
                                figsize=(len(self._individual_vars) / 2, 10),
                                gridspec_kw={'width_ratios': [len(separate_vars),
@@ -913,13 +916,13 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
             first_box_width = 2.
 
         sns.boxplot(x=self._x_name, y="data", hue="ahead", data=data_first, ax=ax[0], whis=1., palette="Blues_d",
-                    showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"},
+                    showmeans=True, order=order_first, meanprops={"markersize": 1, "markeredgecolor": "k"},
                     flierprops={"marker": "."}, width=first_box_width
                     )
         ax[0].set(ylabel=f"skill score", xlabel="")
 
         sns.boxplot(x=self._x_name, y="data", hue="ahead", data=data_second, ax=ax[1], whis=1., palette="Blues_d",
-                    showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"},
+                    showmeans=True, order=order_second, meanprops={"markersize": 1, "markeredgecolor": "k"},
                     flierprops={"marker": "."},
                     )
         ax[1].set(ylabel="", xlabel="")
@@ -984,12 +987,16 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
 
         """
         number_of_vars = len(self._individual_vars)
+        order = self.set_order_for_x_axis(self._individual_vars)
+
+
         if number_of_vars > 20:
             fig, ax = plt.subplots(figsize=(number_of_vars/2, 10))
         else:
             fig, ax = plt.subplots()
         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": "."})
+                    showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"}, flierprops={"marker": "."},
+                    order=order)
         ax.axhline(y=0, color="grey", linewidth=.5)
         plt.xticks(rotation=45, horizontalalignment="right")
         ax.set(ylabel=f"skill score", xlabel="", title="summary of all stations")
@@ -997,6 +1004,28 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
         ax.legend(handles, self._labels)
         plt.tight_layout()
 
+    @staticmethod
+    def set_order_for_x_axis(var_list, sect_split_name="Sect"):
+        try:
+            split_names = [i.split(sect_split_name) for i in var_list]
+            center_name = []
+            sector_identifier = []
+            for i in split_names:
+                if len(i) == 1:
+                    center_name += i
+                else:
+                    sector_identifier.append(i[1])
+            center_name = sorted(center_name)
+            sector_identifier = sorted(list(set(sector_identifier)))
+            new_order = []
+            for center in center_name:
+                new_order += [center] + [center+sect_split_name+sec for sec in sector_identifier]
+        except:
+            new_order = None
+
+        return new_order
+
+
 
 @TimeTrackingWrapper
 class PlotTimeSeries: