3 papers
cs.LG2026
How Can Machine Learning Emulators Best Support Climate Science?
Luca Schmidt, Nina Effenberger, Vitus Benson +5
The paper examines how machine‑learning emulators can be designed and deployed to reduce the computational cost of physics‑based climate models, proposing a framework that emphasiz…
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…