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. 00281v1 Announce Type: cross Abstract: Generative machine learning is an increasingly important complement to dynamical downscaling for producing high-resolution precipitation projections, with diffusion models currently the leading approach.
By Tom Wetherell
arXiv:2602. 13416v2 Announce Type: replace Abstract: The proliferation of data-driven models in weather and climate sciences has marked a significant paradigm shift, with advanced models demonstrating exceptional skill in medium-range forecasting.
By Haiwen Guan, Dibyajyoti Chakraborty, Moein Darman, Troy Arcomano, Ashesh Chattopadhyay, Romit Maulik
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:2609.39833v1 Announce Type: new
Abstract: Extreme rainfall events are increasing in intensity and frequency as climate change accelerates. While kilometer-scale precipitation forecasts are crit...
By Pierre-Louis Lemaire, Luca Schmidt, Wietze Suijker, Alex Hernandez-Garcia, David Rolnick
The paper introduces a scale‑recursive rectified flow that first generates broad rainfall patterns and then refines local details, allocating sampling steps based on ensemble variability and prediction error across spatial scales. This approach reduces the number of sampling steps while maintaining ensemble diversity, improving probabilistic accuracy and rain detection in satellite‑to‑radar downscaling over the United States. The method outperforms a non‑recursive flow with more steps, achieving better accuracy in less time.
By Shunya Nagashima, Takumi Bannai