diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index e52c60f06f152908aec504f6725f67b9d3e6f1a0..10cde645eed44e440f5687d6b5498b5add3ea98d 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -694,13 +694,15 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
         calculated comparisons for cnn, persistence and ols.
     :param plot_folder: path to save the plot (default: current directory)
     :param model_setup: architecture type (default "CNN")
+    :param sampling: sampling of data (default "daily") used for legend
 
     """
 
-    def __init__(self, data: pd.DataFrame, plot_folder=".", model_setup="NN"):
+    def __init__(self, data: pd.DataFrame, plot_folder=".", model_setup="NN", sampling="daily"):
         """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._data = self._prepare_data(data)
         default_plot_name = self.plot_name
@@ -734,7 +736,7 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
         data = data.rename({"stations_level_0": "stations", "stations_level_1": "comparison"})
         data = data.to_dataframe("data").unstack(level=1).swaplevel()
         data.columns = data.columns.levels[1]
-        self._labels = [str(i) + "d" for i in data.index.levels[1].values]
+        self._labels = [str(i) + self._sampling for i in data.index.levels[1].values]
         data = data.stack(level=0).reset_index(level=2, drop=True).reset_index(name="data")
         return data.astype({"comparison": str, "ahead": int, "data": float})
 
@@ -816,7 +818,8 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
 
     """
 
-    def __init__(self, data: Dict, plot_folder: str = ".", model_setup: str = "", separate_vars: List = None):
+    def __init__(self, data: Dict, plot_folder: str = ".", model_setup: str = "", separate_vars: List = None,
+                 sampling="daily"):
         """
         Set attributes and create plot.
 
@@ -828,6 +831,7 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
         super().__init__(plot_folder, f"skill_score_bootstrap_{model_setup}")
         if separate_vars is None:
             separate_vars = ['o3']
+        self.sampling = sampling
         self._labels = None
         self._x_name = "boot_var"
         self._data = self._prepare_data(data)
@@ -851,7 +855,8 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
         data = helpers.dict_to_xarray(data, "station").sortby(self._x_name)
         new_boot_coords = self._return_vars_without_number_tag(data.coords['boot_var'].values, split_by='_', keep=1)
         data = data.assign_coords({'boot_var': new_boot_coords})
-        self._labels = [str(i) + "d" for i in data.coords["ahead"].values]
+        sampling_code = self._get_sampling(self.sampling)
+        self._labels = [str(i) + sampling_code for i in data.coords["ahead"].values]
         if "station" not in data.dims:
             data = data.expand_dims("station")
         return data.to_dataframe("data").reset_index(level=[0, 1, 2])