Statistical downscaling of climate models for the Mediterranean region combining convolutional neural network and quantile delta mapping

Regional climate projections are essential for guiding local governments in developing effective mitigation strategies. A common technique for downscaling General Circulation Model (GCM) outputs is dynamical downscaling, but its high computational demands have motivated the search for alternative approaches, including statistical downscaling. This study presents a two-phase statistical downscaling framework to improve the spatial resolution and accuracy of precipitation and temperature projections. In the first phase, a Convolutional Neural Network (CNN), trained to learn spatial patterns from ERA5 reanalysis data, is employed to refine the coarse grid of GCMs. In the second phase, bias correction is performed using a quantile delta mapping technique, with ERA5 still serving as the reference dataset. The resulting downscaling framework is applied to outputs from five GCMs participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways (SSPs): SSP1-2.6 and SSP3-7.0. This work is part of the OurMED PRIMA project, which focuses on the Mediterranean region, a recognized climate change hotspot. Results indicate substantial improvements in the accuracy of temperature and precipitation projections compared to other downscaling methods. The proposed approach effectively captures fine-scale spatial variability, a crucial aspect for regional climate studies in complex regions like the Mediterranean region. The downscaled climate data are used to assess climate extremes by computing the indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). These indices can offer valuable insights into evolving climate trends and extremes throughout the 21st century. The proposed methodology demonstrates significant potential for broader applications in regions requiring high-resolution climate data to support adaptation strategies and policy development.

This work was supported by OurMED PRIMA Program project funded by the European Union’s Horizon 2020 research and innovation under grant agreement No. 2222. Valerio Todaro acknowledges financial support from the PNRR MUR project ECS_00000033_ECOSISTER.