Severe heatwaves in urban areas significantly threaten public health, calling for establishing early warning strategies. Despite predicting occurrence of heatwaves and attributing historical mortality, predicting an incoming deadly heatwave remains a challenge due to the difficulty in defining and estimating heat-related mortality. Furthermore, establishing an early warning system imposes additional requirements, including data availability, spatial and temporal robustness, and decision costs. To address these challenges, we propose DeepTherm, a modular early warning system for deadly heatwave prediction without requiring heat-related mortality history. By highlighting the flexibility of deep learning, DeepTherm employs a dual-prediction pipeline, disentangling baseline mortality in the absence of heatwaves and other irregular events from all-cause mortality. We evaluated DeepTherm on real-world data across Spain. Results demonstrate consistent, robust, and accurate performance across diverse regions, time periods, and population groups while allowing trade-off between missed alarms and false alarms.
@article{arxiv.2512.09074,
title = {Modular Deep-Learning-Based Early Warning System for Deadly Heatwave Prediction},
author = {Shangqing Xu and Zhiyuan Zhao and Megha Sharma and José María Martín-Olalla and Alexander Rodríguez and Gregory A. Wellenius and B. Aditya Prakash},
journal= {arXiv preprint arXiv:2512.09074},
year = {2025}
}