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
Accurate regional near-surface temperature forecasting is fundamental to short-range weather services and downstream risk assessment. Existing deep learning-based regional forecasters commonly produce...
arXiv:2606. 13119v1 Announce Type: cross Abstract: Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy.
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arXiv:2607. 16731v1 Announce Type: new Abstract: Marine biogeochemical forecasting is increasingly important for managing marine ecosystems and the carbon cycle, yet global, seasonal forecast products lag far behind physical oceanography, held back by the complexity of the processes involved and by data scarcity.
By Gabriela Martinez Balbontin, Anastase Charantonis, Dominique Bereziat, Stefano Ciavatta
arXiv:2607. 11896v1 Announce Type: cross Abstract: Forecasting particulate matter (PM10) requires both station-scale accuracy and continuous spatial fields, especially during severe dust storms.
By Shuangshuang He, Shuo Wang
arXiv:2607. 27152v1 Announce Type: new Abstract: Accurate intraday forecasts of offshore wind are becoming increasingly important for power system operation and the integration of growing shares of offshore wind energy.
By Francesco Pinto, Luca Lanzilao, Paco Lopez Dekker, Angela Meyer
WeatherNext 3 is a new AI‑driven global weather model that improves both spatial and temporal resolution by generating hourly forecasts at 0.1° resolution, matching the best physics‑based models. It incorporates low‑latency geostationary satellite data and learns to predict satellite‑derived precipitation, tropical cyclones, and station observations, enabling 2 m temperature and dewpoint predictions anywhere and anytime. By directly using raw observations instead of relying solely on analysis data, WeatherNext 3 sets a new state‑of‑the‑art for probabilistic medium‑range forecasting skill.
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