arXiv AI By Loys Masquelier, Etienne Le Naour

Beyond Pointwise Error: A Multi-Metric Evaluation of Spatial Climate Downscaling

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arXiv Machine Learning
Sep 25

Lightweight Probabilistic Downscaling from a Deterministic Base Model

The paper introduces lightweight probabilistic downscaling models that build on a modified U‑Net backbone, adapting two recent machine learning techniques from weather forecasting. Using a two‑stage training curriculum—deterministic pretraining followed by probabilistic fine‑tuning—the authors evaluate their models on the CORDEX‑ML‑Bench suite for daily maximum temperature and precipitation in the Alps, New Zealand, and South Africa. The results show that this approach outperforms the current state‑of‑the‑art in RMSE, offering a more computationally efficient method for generating fine‑resolution regional climate data.

By Joseph McLean, Tiffany Vlaar, Sigrid Passano Hellan, Linus Ericsson
arXiv Machine Learning
2d ago

Less is more: error-distance scaling relation for data-efficient kilometer-scale downscaling of extreme heat

arXiv:2609.40140v2 Announce Type: cross Abstract: Extreme heat is where urban adaptation needs kilometer-scale data the most, but the simulations training a downscaler can cost more than they save, a...

By Ahmed Marey, Henry Lu, Abhishek Gaur, Sherif Goubran, Malek Aloui, Theodore Potsis, David Rolnick, Alex Hernandez-Garcia, Liangzhu Leon Wang