Index: trunk/tests/test_acceptance.py
===================================================================
diff -u -r192 -r193
--- trunk/tests/test_acceptance.py	(.../test_acceptance.py)	(revision 192)
+++ trunk/tests/test_acceptance.py	(.../test_acceptance.py)	(revision 193)
@@ -202,9 +202,10 @@
         steplat = 0.25
         latl = 52.5
         latu = 57
-        x_range = np.arange(lonl, lonr + steplon, steplon).tolist()
-        y_range = np.arange(latl, latu + steplat, steplat).tolist()
-        coordsBOX = {"LON": x_range, "LAT": y_range}
+        coordsBOX = {
+            "LON": np.arange(lonl, lonr + steplon, steplon).tolist(),
+            "LAT": np.arange(latl, latu + steplat, steplat).tolist(),
+        }
         timeWAMy = list(range(1981, 1987))
         # use the SDToolBox function to create input data
         Input_DataBOX = InputData(
@@ -221,6 +222,36 @@
         # assert extracted_data.variables["msl"].any(), "No values generated for key msl."
         ChunkDataUtils.assert_valid_ndarray(extracted_data.variables["msl"])
 
+    @pytest.mark.parametrize(
+        "variable_key", [pytest.param("swh"), pytest.param("msl"),],
+    )
+    def test_given_gridded_test_case_lat_lon_returned_correctly(
+        self, variable_key: str
+    ):
+        steplon = 0.5
+        lonl = 3
+        lonr = 7.5
+        steplat = 0.5
+        latl = 52.5
+        latu = 57
+        coordsBOX = {
+            "LON": np.arange(lonl, lonr + steplon, steplon).tolist(),
+            "LAT": np.arange(latl, latu + steplat, steplat).tolist(),
+        }
+        timeWAMy = list(range(1981, 1987))
+        Input_DataBOX = InputData(
+            input_coordinates=coordsBOX,
+            input_variables=[variable_key],
+            input_scenarios=["era5"],
+            input_years=timeWAMy,
+            is_gridded=True,
+        )
+
+        dir_test_data = TestUtils.get_local_test_data_dir("chunked_data")
+        extracted_data = ExtractData.get_era_5(dir_test_data, Input_DataBOX)
+        assert extracted_data.data_dict["lat"] == coordsBOX["LAT"]
+        assert extracted_data.data_dict["lon"] == coordsBOX["LON"]
+
     def test_extract_gridded_data_multivariable(self):
         chunked_lon, chunked_lat = ChunkDataUtils.get_default_chunked_lon_lat()
         coordsBOX = {"LON": chunked_lon, "LAT": chunked_lat}
Index: trunk/SDToolBox/extract_data.py
===================================================================
diff -u -r192 -r193
--- trunk/SDToolBox/extract_data.py	(.../extract_data.py)	(revision 192)
+++ trunk/SDToolBox/extract_data.py	(.../extract_data.py)	(revision 193)
@@ -245,22 +245,22 @@
 
             # Set the nearest neighbors and the time reference.
             logging.info("Getting nearest neighbors.")
-            nn_idx, nn_values = self.__get_nearest_neighbors_lon_lat(
+            nn_idx, nn_values = self.__get_nearest_neighbors_lat_lon(
                 ref_file_path=self.__first_filepath,
                 input_data=input_data,
                 cases_dict=output_data.data_dict,
             )
             logging.info(
-                f"Nearest neighbors: \nLon:{nn_values[0].astype(str)} \nLat:{nn_values[1].astype(str)}"
+                f"Nearest neighbors: \nLat:{nn_values[0].astype(str)} \nLon:{nn_values[1].astype(str)}"
             )
 
             nn_idx, idx_map = self.__get_unique_indices(nn_idx)
-            output_data.data_dict[output_data.var_lon_key] = [
+            output_data.data_dict[output_data.var_lat_key] = [
                 value
                 for idx, value in enumerate(nn_values[0].tolist())
                 if idx in idx_map
             ]
-            output_data.data_dict[output_data.var_lat_key] = [
+            output_data.data_dict[output_data.var_lon_key] = [
                 value
                 for idx, value in enumerate(nn_values[1].tolist())
                 if idx in idx_map
@@ -379,7 +379,7 @@
                 axis=int(not input_data.is_gridded),
             )
 
-        def __get_nearest_neighbors_lon_lat(
+        def __get_nearest_neighbors_lat_lon(
             self, ref_file_path: str, input_data: InputData, cases_dict: dict
         ) -> Tuple[Tuple[List[int], List[int]], Tuple[np.array, np.array]]:
             """Gets the corrected index and value for the given
@@ -441,8 +441,8 @@
                 is_gridded=input_data.is_gridded,
             )
             return (
-                (nn_lon_idx, nn_lat_idx),
-                (np.array(nn_lon_values), np.array(nn_lat_values)),
+                (nn_lat_idx, nn_lon_idx),
+                (np.array(nn_lat_values), np.array(nn_lon_values)),
             )
 
         def _set_nn(
@@ -508,6 +508,17 @@
         def get_masked_nn_lat_lon(
             self, input_data: InputData, ref_dataset: Dataset, mask_dir: Path,
         ) -> Tuple[Tuple[List[int], List[int]], Tuple[List[float], List[float]]]:
+            """Returns the masked latitude and longiuted of a givendataset.
+
+            Args:
+                input_data (InputData): Input data with coordinates.
+                ref_dataset (Dataset): Dataset that needs to be masked.
+                mask_dir (Path): Direction to the mask directory.
+
+            Returns:
+                Tuple[Tuple[List[int], List[int]], Tuple[List[float], List[float]]]:
+                    Index of nearest neighbors and a Tuple of corrected Latitude and Longitude
+            """
             mask_filepath = Path(mask_dir) / "ERA5_landsea_mask.nc"
             possea, LON, LAT = ExtractData.get_seamask_positions(mask_filepath)
             index, vals = self.get_nearest_neighbor_extended(