Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches

Due to limitations in the computational resources available, General Circulation Models (GCMs) are advocated to simulate the climate system over coarse resolution grids. This hampers the applicability of GCM products in the regional-to-local scale, highly demanded by different socio-economic sectors. Statistical downscaling aims to solve this problem by generating high-resolution climate fields. Recently, machine learning techniques (particularly deep learning models) have shown promising results in this task. These models are first trained in a historical period through observational datasets, and then applied to the GCM outputs of plausible far-future scenarios, thus generating high-resolution climate change products. To assess the plausibility of the derived downscaled fields, several validation frameworks are performed, (e.g., skill to reproduce the present climate) which aim to assess the generalization of the models. Here, we present a novel evaluation protocol building on eXplainable Artificial Intelligence (XAI) to examine the suitability of certain deep learning models for climate downscaling.