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@@ -222,6 +222,36 @@ The Anomalies module computes the anomalies of the data with respect to the clim
 
 The output of the main function, `Anomalies()`, is a list of `s2dv_cube` objects containing the anomalies for the hcst, fcst and obs, as well as the original hcst and obs full fields in case they are needed for later computations.
 
+## Downscaling Module
+
+The Downscaling module performs downscaling on the anomalies. It accepts the output of the Anomalies module as input and also requires the recipe. The module applies the selected downscaling method to the hindcast anomalies using observed anomalies as the reference and returns the downscaled data and its metadata as an s2dv_cube object.
+
+The output of the main function, **Downscaling()**, is a list containing the downscaled hindcast and observations, named **hcst** and **obs**.
+
+**Downscaling methods currently available:**
+
+The first step is to specify the type of downscaling, choosing from the following options:
+- 'none' 
+- 'analogs'
+- 'int'
+- 'intbc'
+- 'intlr'
+- 'logreg'. 
+
+This specification is a mandatory requirement and must be defined in the recipe under **Workflow:Downscaling:type**. The downscaling method can be further specified through the following sections:
+
+- **Workflow:Downscaling:int_method** # optional; "NULL", 'con', 'bil', 'bic', 'nn', 'con2',"dis","laf".
+- **Workflow:Downscaling:bc_method** # optional; NULL, "quantile_mapping" (or "qm"), "bias", "evmos", "mse_min", "crps_min" or "rpc-based".
+- **Workflow:Downscaling:lr_method** # optional; 'NULL', 'basic', 'large-scale', '4nn'.
+- **Workflow:Downscaling:log_reg_method** # optional; "NULL" ,"ens_mean", "ens_mean_sd", "sorted_members".
+- **Workflow:Downscaling:nanalogs** # optional; number of analogs to be searched.
+
+The user can only request one downscaling type per 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.
+
+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.).
+
 ## Indices module
 
 The Indices module aggregates the hindcast and reference data to compute climatological indices such as the North Atlantic Oscillation (NAO) or El Niño indices.