Multi-Source Data Driven Forecasting of Extreme Heat Events Using an ARIMA–XGBoost Hybrid Framework

Abstract. Extreme heat events (EHEs) pose growing risks to densely populated subtropical cities such as Hong Kong, yet there remains a need for lightweight, interpretable tools that can provide multi-day forecasts based on readily available observations. This study develops a multi-source data driven framework that integrates aerosol optical depth (AOD), land surface temperature (LST), precipitable water (PW), and precipitation (Precip), together with ARIMA-based anomaly features, to predict EHEs over Hong Kong. Using a seven-day sliding window, independent XGBoost classifiers are trained to forecast daily EHE occurrence probabilities for the next 1–5 days over ten climate years (March 2015–February 2025). A lead-specific threshold optimization on a validation subset is applied to maximize F1-score. Test results show that AUC values for Lead 1–Lead 5 remain between 0.935 and 0.883, with F1-scores between 0.738 and 0.639, indicating robust predictability up to five days in advance. A process-scale duration inference method based on the leading continuous segment of the predicted sequence achieves 67.08% exact-match accuracy, 77.69% accuracy within ±1 day, and a mean absolute error of 0.75 days. The proposed framework is computationally efficient and operationally relevant, offering practical support for urban heat early warning and risk management.