Modelling the Dependence Between Drought and Heatwave Extremes in Kenya Using an Integrated EVT-Copula-XGBoost Framework

Compound drought-heatwave events pose serious threats to agriculture, ecosystems, and livelihoods in Kenya, where increasing climate variability amplifies their frequency and intensity. This study developed a hybrid Extreme Value Theory (EVT)-Copula-XGBoost framework to characterize and predict concurrent drought and heatwave extremes using ERA5 reanalysis data (2005-2024).The EVT component modeled the marginal tails of temperature and precipitation, revealing that temperature extremes follow a bounded Weibull-type tail, while rainfall deficits exhibit heavy tails, indicating a high potential for severe droughts. Copula modeling captured the dependence structure between drought and heatwave indices, showing weak but significant negative dependence (Kendall’s τ = −0.189 to 0.034), strongest during the short rains season (SON), implying that hot and dry conditions often co-occur. Joint risk analysis estimated return periods of 2.5-4.7 years, with five-year joint thresholds of 2.3-2.7 mm rainfall and 25.1-25.3◦C temperature, suggesting that compound drought-heatwave events recur roughly every three years. The XGBoost model achieved high predictive skill (AUC = 0.989), with EVT and Copula derived features contributing most to performance. This hybrid framework provides a robust, data driven foundation for early detection, risk mapping, and climate adaptation planning, supporting proactive management of compound climate extremes in Kenya.