Eroding heat resilience in South Asian cities under observed warming trends

Rapidly urbanising South Asian cities face increasing thermal risk, but comparative, physically grounded studies of their structural capacity to withstand heat loading are rare. We utilize a stacked ensemble of five machine learning architectures (Random Forest, Gradient Boosting, XGBoost, LightGBM, and a deep learning model) to forecast daytime land surface temperature (LST) in 15 cities with dry, tropical, montane, and coastal climates. The ensemble achieved R² = 0.971 and RMSE ≈ 1.18 °C. We introduce the Urban Heat Resilience Index (UHRI), a weighted composite of thermal stress, cooling capacity, environmental buffering, and adaptive capacity sub-components, and use it to assess structural resilience discrepancies and estimate their trajectory to 2075. UHRI scores vary nearly twofold, from 35.2 (Delhi) to 67.1 (Thimphu). Critically, high-resilience cities show no significant warming trends, showing that structural resilience provides measurable resistance to LST acceleration. These findings offer a regionally validated, paradigm for prioritising urban heat adaption investment in one of the world’s most thermally exposed urbanizing regions.