*3.2. Downscaling and Bias Correction*

After the shortlisting and ranking of the GCMs, the next step was to address the two primary issues inhibiting impact studies: firstly, the coarse spatial scales represented by the GCM may not be as fine as required by regional- and local-scale environmental modelling or impact studies; and secondly, the GCM raw outputs, or their downscaled versions, are deemed to contain systematic errors (bias) of certain magnitude, relative to the observational data, and therefore need post-processing by correcting it with and towards observations prior their its use in environmental modelling or impact studies.

The downscaling can be done either by applying statistical downscaling methods or through dynamical downscaling via application of a regional climate model (RCM). As dynamical downscaling was too demanding in terms of time and computational resource requirements, we decided only to explore whether any dynamically downscaled RCM projections were available for the already shortlisted and ranked GCMs. The Coordinated Regional Downscaling Experiment (CORDEX) has generated fine-scale climate projections for different regions of the world, of which the CORDEX-South Asia experiments cover the UIB region. We found that CORDEX-RCM model projections were available for four of the selected GCMs at the 1st rank and one GCM at the 2nd rank. These RCM projections provide dynamically downscaled data at a resolution of ~50 km for all of our selected GCMs. The data for the relevant GCM–RCM combinations were downloaded, but needed further downscaling, as the scale was still not fine enough, and also needed to undergo bias correction before further use in hydrological modelling.

This downscaling and bias correction was achieved by the Distribution Mapping method (DM) [43], which was selected out of five different bias correction methods for the precipitation climate variable. These methods included: (1) Linear Scaling (LS); (2) Local Intensity Scaling (LIS); (3) Power Transformation (PT); (4) Distribution Mapping (DM); and (5) Distribution Mapping followed by Intensity and frequency Scaling (DM-IS).

For the temperature, the selection was made after evaluating the performance of the following three bias correction methods: (1) Linear Scaling (LS); (2) Variance Scaling (VS); and (3) Distribution Mapping (DM). Further details of these methods can be found in [43].

The calibration and validation statistics, along with brief explanations, are provided as appendices (Supplementary Materials: Appendix A, Tables A1 and A2).
