A Hybrid Modeling Framework for High-Resolution Humid Heat Mapping in Metropolitan Wuhan: Integrating Physical Simulations and Deep Learning

Under the dual challenges of global warming and rapid urbanization, Wuhan faces escalating thermal risks exacerbated by frequent heatwaves and intensified urban heat island effects. The city's distinctive land use land cover configuration with hundreds of lakes in a basin creates a self-reinforcing thermal trap: extensive water networks elevate humidity through evaporation, while vertical urban canyons restrict ventilation and prolong heat and moisture retention. This study develops an innovative hybrid modeling framework to unravel Wuhan's complex heat-moisture dynamics and generate hourly meter-scale heat index mapping. To overcome existing methodological constraints, where conventional physics-based models face expensive computational cost bottlenecks and data-driven methods face limitations in observational data quality, we present a three-phase framework synergizing physical modeling and deep learning. Phase one enhances urban climate simulations through WRF-UCM optimized with 1km three-dimensional urban canopy parameters and updated land use data, achieving significantly improved temperature accuracy across 183 urban weather stations. Phase two employs a hybrid CNN-Transformer architecture that fuses multi-source data streams, including calibrated WRF outputs, IoT sensor networks, and sub-meter remote sensing layers to predict hyper-resolution HI through spatiotemporal feature fusion. Phase three reveals critical diurnal thermal patterns through SHAP-enhanced interpretability analysis, quantifying water bodies' substantial moisture contribution and identifying high-risk zones in compact urban cores with pronounced HI diurnal fluctuations. Our framework demonstrates superior computational efficiency with high spatial accuracy in HI prediction, establishing the first operational hourly meter-scale heat monitoring system for Wuhan. The methodology advances urban climate modeling through physics-AI hybridization while providing urban planners with three-dimensional heat mitigation insights from blue infrastructure optimization to urban morphology planning, supporting climate-resilient and healthy city design.