Pre-training for Deep Statistical Climate Downscaling: A case study within the Spanish National Adaptation Plan (PNACC)

Abstract. Deep Learning (DL) has recently emerged as a promising approach for statistical climate downscaling. In this study, we investigate the use of pre-training in this context, building on the DeepESD model developed for the Spanish National Adaptation Plan (PNACC), which uses ERA5 predictors and the 5 km ROCIO-IBEB national gridded predictand dataset. We evaluate the effectiveness of different fine-tuning strategies to adapt this pre-trained model to alternative regional predictand datasets, specifically a point-based station dataset. The objective is to develop downstream downscaling methods that maintain consistency with the original national-scale model while capturing the specific characteristics of regional and local datasets.

We analyze the benefits of fine-tuning in terms of faster convergence, improved generalization, and greater consistency. Using eXplainable Artificial Intelligence (XAI) techniques, we examine the relationships learned by the models and compare the resulting climate change signals. Our results demonstrate that pre-training provides a robust foundation for statistical downscaling, particularly in cases with limited spatial and/or temporal data availability (e.g., local high-resolution datasets available only for short periods), thereby reducing epistemic uncertainty and improving the reliability of future climate projections. Overall, this approach represents a step toward standardizing DL-based downscaling models to ensure more coherent and consistent climate projections across national and regional scales.