arXiv:2606. 14570v1 Announce Type: cross Abstract: Emulators provide a cost-effective alternative to regional climate models (RCMs) by capturing their dynamical downscaling function.
By Mikel N. Legasa, Antoine Doury, Achille Gellens, Redouane Lguensat, Clara Naldesi, Soulivanh Thao, Mathieu Vrac
arXiv:2604. 03275v2 Announce Type: replace-cross Abstract: Effective adaptation and mitigation strategies for climate change require high-resolution projections to inform strategic decision-making.
By Kishanthan Kingston, Olivier Boucher, Freddy Bouchet, Pierre Chapel, Rosemary Eade, Jean-Francois Lamarque, Redouane Lguensat, Kazem Ardaneh
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. 17037v1 Announce Type: new Abstract: High-resolution atmospheric data are required to resolve mesoscale and localized meteorological structures, however such datasets remain limited in many regions of the world.
By Evangelia Rafaela Frastali, Achyut Paudel, Maryam Golbazi, Frank Liu
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
The study compares the Weather Research and Forecasting (WRF) dynamical model with an unpaired diffusion-based generative model for downscaling extreme precipitation events up to three weeks ahead. Both models outperform raw European Centre for Medium-Range Weather Forecasts forecasts when evaluated against Swiss rain gauge-radar observations, but their strengths differ by atmospheric regime: WRF excels in a multicell, non‑stationary event, while the diffusion model performs more consistently and better in a stationary supercell event.
By Mauricio Lima, Marika Koukoula, Romain Pilon, Monika Feldmann, Erwan Koch, Daniela I. V. Domeisen, Tom Beucler