3 papers
physics.ao-ph2026
CORDEX-ML-Bench: A Benchmark for Data-Driven Regional Climate Downscaling -Experiment Design and Overview
Neelesh Rampal, José González-Abad, Henry Addison +34
Machine learning (ML) has emerged as a cost-effective approach to complement dynamical downscaling for producing high-resolution regional climate projections. However, the absence…
physics.ao-ph2026
Regional Climate Model Emulation with Diffusion Approaches: What is the Added Value of Generative Machine Learning?
Mikel N. Legasa, Antoine Doury, Achille Gellens +4
Emulators provide a cost-effective alternative to regional climate models (RCMs) by capturing their dynamical downscaling function. They link large-scale predictors simulated by gl…
cs.LG2026
How Can Machine Learning Emulators Best Support Climate Science?
Luca Schmidt, Nina Effenberger, Vitus Benson +5
For decades, physics-based climate models have been used to provide insights for climate decision-making. Their application is, however, constrained by significant computational an…