The paper introduces PRISMA, a generative framework that separates precipitation prior training from sensor-specific constraints, allowing flexible composition of heterogeneous satellite observations without retraining the core model. By integrating FY‑4B/AGRI, GPM/GMI, F16‑F18 SSMIS, and GPM/DPR‑Ka data, PRISMA consistently improves precipitation‑estimation accuracy and outperforms IMERG Final in CRPS and RMSE while maintaining positive Brier skill across thresholds. The framework supports rapid, accurate ensemble precipitation estimates, enhancing satellite‑based monitoring for hydrometeorological hazards.
By Yunfan Yang, Haofei Sun, Xiuyu Sun, Wei Han, Xiaoze Xu, Xingtao Song, Jun Li, Zhiqiu Gao, Wei Huang
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 CD-FFM, a functional flow‑matching model that treats high‑resolution climate fields as continuous random fields, allowing queries at any location and resolution. Unlike grid‑based generative downscalers, CD‑FFM uses a pointwise velocity field and a noise prior fitted to the observed spectrum, giving each observation an exact likelihood. Applied to downscaling ERA5 temperature to 1 km over Fennoscandia, CD‑FFM improves the continuous ranked probability score by 27–31 % over regression and 12–18 % over a larger neural operator, while being about fifty times faster than a CorrDiff‑style diffusion model and also effective for precipitation.
By Zhi-Song Liu, Helmi Toropainen, Ammar Kheder, Michael Boy
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
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:2608.30205v1 Announce Type: new
Abstract: Localized extreme precipitation is a major trigger of urban flash floods and landslides, yet producing nowcasts that combine fine spatial detail with p...
By Dohyun Park, Changhoon Song, Tengyuan Chang, Yoo-Geun Ham, Youngjoon Hong
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
The paper introduces a plug‑and‑play framework called Dynamics‑Precipitation Decoupling that employs a diffusion generator to convert non‑precipitation forecasts from a frozen weather model into observation‑aligned daily precipitation. The generator is conditioned on atmospheric states and satellite‑gauge precipitation estimates, using a multi‑scale recurrent encoder and cross‑attention to capture heterogeneous rainfall distributions and align daily accumulations with evolving atmospheric signals. Experiments on global and regional datasets, including a heavy‑rainfall case study in China, show that the method improves daily precipitation accuracy, event detection, and localized accumulation patterns compared to several baseline weather models and ensemble forecasts.
By Guowen Li, Yang Liu, Mengxuan Chen, Jiashun Cheng, Haoyuan Liang, Shilei Cao, Yujie Wang, Juepeng Zheng, Fang Wang, Yerong Feng, Meng Jin, Hong Cheng, Yutong Lu, Haohuan Fu
arXiv:2511. 23043v2 Announce Type: replace-cross Abstract: We present a probabilistic data-driven weather model providing ensembles of high spatial resolution realizations of 87 variables at arbitrary ensemble size and forecast length.
By Even Marius Nordhagen, H{\aa}vard Homleid Haugen, Magnus Sikora Ingstad, Aram Farhad Shafiq Salihi, Thomas Nils Nipen, Ivar Ambj{\o}rn Seierstad, Inger-Lise Frogner, Mariana Clare, Simon Lang, Matthew Chantry, Peter Dueben, J{\o}rn Kristiansen
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
PCSDiff is a diffusion-based framework designed to correct systematic biases and enhance spatial resolution in medium-term (10‑day) precipitation forecasts. It uses a Precipitation Intensity‑aware Multi‑branch Decoder to mitigate dynamic multi‑day errors and a two‑phase conditional diffusion super‑resolution module to restore fine‑scale rainfall patterns. Evaluated over China, PCSDiff reduces RMSE by 16.1% and increases ACC by 13.9% compared to raw ECMWF forecasts, outperforming mainstream deep‑learning baselines and enabling low‑latency rolling forecasts for operational use.
By Yuze Sun, Shiyi Wang, Jiancheng Pan, Die Wang, Andreas F. Prein, Wentao Luo, Linhan Jiang, Jie Wu, Quan Zhang, Xiaomeng Huang
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