arXiv:2606. 07898v1 Announce Type: new Abstract: High-resolution regional climate simulations provide critical information for climate impacts assessments but remain computationally expensive, motivating the development of machine-learning downscalers and emulators.
By Karandeep Singh, Stefan Rahimi, Chad W. Thackeray, Stephen Cropper, Alex Hall
arXiv:2607. 03279v1 Announce Type: new Abstract: Accurate regional weather prediction requires resolving fine-scale structure while remaining consistent with global dynamics.
By Wiktor Kamzela, Jakub Kubiak, Adam Dobosz, J\k{e}drzej Miczke, Anatol Kaczmarek, Piotr Wyrwi\'nski, Wojciech Stefaniak, Wojciech Kot{\l}owski
arXiv:2605. 16163v2 Announce Type: replace-cross Abstract: Skillful medium-range precipitation forecasting at kilometer scale remains challenging over complex terrain because precipitation arises from multiscale nonlinear processes that global models cannot explicitly resolve at affordable cost.
By Dan Assouline, Erwan Koch, Federico Amato, Filippo Quarenghi, Daniele Nerini, Thibaut Loiseau, Kyle van de Langemheen, Tom Beucler
arXiv:2608. 14652v1 Announce Type: cross Abstract: The development of 0.
By Yang Zhao, Peisong Niu, Tian Zhou, Ziqing Ma, Guanlong Ma, Rong Jin, Huiling Yuan, Liang Sun
arXiv:2609.08412v1 Announce Type: new
Abstract: Machine-learning weather models (MLWMs) now match or outperform operational numerical weather prediction (NWP) at global medium-range forecasting, at f...
By Simon Adamov, Oliver Fuhrer, Reto Knutti, Sebastian Schemm
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