NPJ Clim Atmos Sci. 2026;9(1):151. doi: 10.1038/s41612-026-01424-6. Epub 2026 May 4.
ABSTRACT
Statistical downscaling translates coarse-resolution climate model output into locally relevant information for climate services and impact assessment. Recent advances in artificial intelligence (AI) enable high-resolution, probabilistic, and computationally efficient approaches. This paper provides a perspective on the evolution from classical to AI-driven and hybrid downscaling approaches, assesses key challenges related to interpretability, uncertainty, data availability, and computational requirements, and outlines physically constrained and generative frameworks that support decision-making across sectors.
PMID:42428801 | PMC:PMC13345914 | DOI:10.1038/s41612-026-01424-6
