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@@ -272,6 +272,8 @@ 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.
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 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.
 
 The **Workflow:Downscaling:size** argument can be used, if the type of downscaling is analogs and the input dataset is of daily/daily_mean frequency. It indicates the window size (in terms of days) along which the analogs will be searched (target_month -(+) size days). To utilize this argument, the user is required to supply observed anomalies for both the preceding and succeeding months, in addition to the target month. The input data must be provided within the `smonth` dimension of the observed dataset, with the centralization of the target month (i.e.,, the `smonth` dimension should be 3 for the observation, and the second dimension of smonth should correspond to the data for the target month.).