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@@ -272,7 +272,7 @@ This specification is a mandatory requirement and must be defined in the recipe
 
 When selecting the downscaling method `'intbc'`, both interpolation and bias correction methods should be specified; for 'intlr,' both interpolation and linear interpolation methods are required; and for 'logreg,' both interpolation and logistic regression methods should be provided. Leave-one-out cross-validation is always applied for all the methods in the module.
 
-For the analogs method, the downscaling can also be applied by using a large scale variable as the predictor. In this scenario, the function identifies the day in the observation data that closely resembles the large-scale pattern of interest in the model. When it identifies the date of the best analog, the function extracts the corresponding local-scale variable for that day from the observation of the local scale variable. The used local-scale and large-scale variables can be retrieved from independent regions. If this approach is desired to be used, in addition to local-scale observations (obs), observation, and hindcast (forecast) data of the large-scale variable should also be provided. For example, when downscaling is performed via the large-scale variable, the s2dv_cube objects that need to be provided within the list object (i.e., "data" in this example) are as follows: data\$obs, data\$obsL, data\$hcstL or data\$fcstL (if forecast downscaling is aimed).
+For the analogs method, the downscaling can also be applied by using a large scale variable as the predictor. In this scenario, the function identifies the day in the observation data that closely resembles the large-scale pattern of interest in the model. When it identifies the date of the best analog, the function extracts the corresponding local-scale variable for that day from the observation of the local scale variable. The used local-scale and large-scale variables can be retrieved from independent regions. If this approach is desired to be used, in addition to local-scale observations (obs), observation, and hindcast (forecast) data of the large-scale variable should also be provided. For example, when downscaling is performed via the large-scale variable, the s2dv_cube objects that need to be provided within the list object (i.e., "data" in this example) are as follows: data\$obs, data\$obsL, data\$hcstL (data\$fcstL; if forecast downscaling is aimed).
 
 Another option in the recipe is **Workflow:Downscaling:target_grid**. This argument is a character vector indicating the target grid (i.e., to which grid system the dataset will be downscaled). It can be the path to a netCDF file or a grid string or grid description file accepted by CDO.