AI-Enhanced Subseasonal Forecasting of Extreme Temperature Risks

Sub-seasonal weather prediction remains a significant scientific challenge due to the chaotic nature of the atmosphere, with current numerical and AI-driven models exhibiting limited skill, particularly at the fine spatial scales for human exposure, agriculture, and infrastructure. Here, we introduce DeepMet, a high-resolution, AI-driven sub-seasonal forecasting system designed to improve the prediction of temperature extremes and their associated health risks, demonstrated successfully over the continental United States. Specifically, DeepMet substantially outperforms the benchmark of European Centre for Medium-Range Weather Forecasts, reducing the root mean square error by 20–60% for key surface variables, including daily maximum and minimum 2-meter temperature, specific humidity, and 10-meter wind speed. The model also improves the detection of extreme heat and cold events by over 40% across all evaluation metrics. By enhancing early warning capabilities, DeepMet enables more accurate identification of extreme weather conditions, potentially improving risk communication to prevent additional extreme-weather related deaths in the United States. Remarkably, such performance is achieved using only a single GPU for training, making the method highly accessible for local agencies to enhance early warning systems and protect public health. This underscores its strong potential to transform long-range forecasting and significantly enhance public health preparedness in a changing climate.