diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index 491aa52e0a9fe0010f77cde315d1f9b7ddb76dfb..cd898e9e03bc60df481011524ead0aa674f0effa 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -81,6 +81,8 @@ class PlotMonthlySummary(AbstractPlotClass):
             data_nn = data.sel(type=self._model_name).squeeze()
             if len(data_nn.shape) > 1:
                 data_nn = data_nn.assign_coords(ahead=[f"{days}d" for days in data_nn.coords["ahead"].values])
+            else:
+                data_nn.coords["ahead"].values = str(data_nn.coords["ahead"].values) + "d"
 
             data_obs = data.sel(type="obs", ahead=1).squeeze()
             data_obs.coords["ahead"] = "obs"
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index 0d7bfeb4c411eeeb4550bf33e187053ca84cd551..2c31fba97a6ee542cb706c55e7fa11c948f69a40 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -533,7 +533,14 @@ class PostProcessing(RunEnvironment):
         """
         tmp_ols = self.ols_model.predict(input_data)
         target_shape = ols_prediction.values.shape
-        ols_prediction.values = np.swapaxes(tmp_ols, 2, 0) if target_shape != tmp_ols.shape else tmp_ols
+        if target_shape != tmp_ols.shape:
+            if len(target_shape)==2:
+                new_values = np.swapaxes(tmp_ols,1,0)
+            else:
+                new_values = np.swapaxes(tmp_ols, 2, 0)
+        else:
+            new_values = tmp_ols
+        ols_prediction.values = new_values
         if not normalised:
             ols_prediction = transformation_func(ols_prediction, "target", inverse=True)
         return ols_prediction