A deep‑learning model that converts dynamical circulation forecasts into precipitation estimates predicts a dry anomaly over central China in summer 2026, with consistent signals from March to May. Retrospective tests show the model performs best in analogue years marked by sustained central equatorial Pacific warming, which promotes a cyclonic circulation that drives northerly winds and moisture divergence, suppressing rainfall. Layer‑wise relevance propagation identifies these northerly winds as the key driver, and perturbation tests confirm that removing them eliminates the dry anomaly, demonstrating a physically interpretable link between AI predictions and climate dynamics.
A deep learning model that converts dynamical circulation forecasts into precipitation estimates predicts a dry anomaly over central China in the summer of 2026, with consistent signals from March to May. Retrospective tests show the model performs best in analogue years marked by sustained central equatorial Pacific warming, which promotes a cyclonic circulation that drives northerly winds and moisture divergence, suppressing rainfall. Layer‑wise relevance propagation identifies these northerly winds as the key driver, and perturbation tests confirm that removing them eliminates the predicted dry anomaly, providing a physically interpretable explanation for the AI forecast.
By Anran Wang, Wen Shi, Yong Luo, Jianbin Huang, Lijuan Chen, Junhu Zhao, Weixin Jin, Huihui Yuan
arXiv:2609.24882v1 Announce Type: new
Abstract: Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from hi...
By Jurij Sch\"onfeld, Tom Beucler, Julien Savre, Steven Sherwood, Veronika Eyring
The paper introduces an explainable contrastive learning model that maps five monthly cloud and radiation fields into a shared representation space to analyze perturbed parameter ensembles (PPEs) in climate simulations. Trained on two 100-member Community Atmosphere Model version 6 PPEs differing only in the warm rain microphysics scheme (KK2000 vs. TAU-ML), the model achieves over 94% linear classification accuracy while preserving seasonal variability and ensemble spread. The learned representations place satellite observations on the same low‑dimensional manifold as the PPEs, with TAU-ML PPE closer to observations, and Integrated Gradients attribution identifies key regional contributions linked to cloud microphysics, boundary layer turbulence, and deep convection.
By Da Fan, David John Gagne II, Gregory S Elsaesser, Brian Medeiros, Addisu G Semie, Qingyuan Yang, Akila Sampath, Subashree Venkatasubramanian
MAGPIE‑Net is a deep‑learning framework that directly maps multitemporal FY‑4A AGRI infrared and water‑vapor observations to short‑duration heavy‑rainfall warnings for irregular station neighborhoods. By embedding a geographically adaptive, differentiable grid‑to‑station mapping and training with station‑neighborhood event losses, the model outperforms traditional gridded‑precipitation baselines, achieving higher detection rates and longer lead times in 2023 warm‑season tests over China.
By Xiang Lin, Yunying Li, Chengzhi Ye, Zitong Chen, Jing Sun
We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resol...
The paper presents Laxmi, a retrained version of the AIFS weather model that uses satellite-based precipitation observations instead of ERA5 reanalysis data. Laxmi achieves a 19% improvement in global probabilistic accuracy, reduces drizzle overprediction by 33%, and boosts the 95th percentile Brier skill score by 57%. In a case study of 10 Indian tropical storms, Laxmi accurately forecasted 150 mm event-total precipitation in 7 events, outperforming both the original AIFS and the leading physical model IFS.
By Julian F. Schmitt, Bertrand Delorme, Robert C. King, Yashica Patodia, Tapio Schneider, Aditi Sheshadri, Ravi Jain
arXiv:2607. 16080v1 Announce Type: new Abstract: Precipitation nowcasting over the immediate 10-90 min period is important for flood management and real-time decision-making in urban regions.
By Akshay Sunil, Muhammed Rashid, Raja Sekhar Sivaraju, Sushma Nair, Subimal Ghosh
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:2510. 20769v2 Announce Type: replace-cross Abstract: Accurate medium-range precipitation forecasting is essential for hydrometeorological risk management but remains challenging for both numerical weather prediction (NWP) systems and data-driven models.
By Tianyi Xiong, Haonan Chen, Kelly Mahoney, Jingyin Tang, Tim Smith, Janice Bytheway
arXiv:2607. 26581v1 Announce Type: new Abstract: Convolutional long short-term memory networks (ConvLSTMs) are widely used for precipitation forecasting, but most evidence for their performance comes from dense, high-frequency radar sequences.
By Tanmay Ghosh, Shaurabh Anand, Rakesh Gomaji Nannewar, Nithin Nagaraj
arXiv:2608.12685v2 Announce Type: replace-cross
Abstract: An ``Attention Residual U-Net'' method is described for probabilistic quantitative precipitation forecasting (PQPF) that predicts the hourly...
By Thomas M Hamill