Pre-training for deep statistical climate downscaling: enhancing consistency and robustness across regional datasets

Abstract

We explore how deep learning can improve local climate projections by adapting a national model to regional data. By relying on a paradigm called pre-training, we show that models can produce more consistent and physically aligned results, even when data is limited. This helps make future climate projections more reliable and supports better planning at both national and local levels.