Machine Learning–Enhanced Detection of Climate Regime Shifts in the West African Sahel

Climate breakpoint detection is fundamental to understanding regime shifts in hydroclimatic systems, yet the dominant statistical tests (Pettitt, Buishand, and SNHT) assume single change-points and offer limited diagnostic capacity. This study tests a Machine Learning (ML) Ensemble framework combining Random Forest and Gradient Boosting classifiers trained on 13 engineered temporal features (CUSUM, rolling divergence statistics, local trend slopes, and rank-based indicators) to detect and characterize multiple breakpoints in climate time series. The approach is applied to monthly temperature and precipitation records from 12 stations spanning Senegal’s diverse climatic zones (1975-2025), alongside classical tests and kernel-based changepoint methods (PELT, Binary Segmentation). Results reveal a pronounced and statistically robust thermal breakpoint concentrated around 1994-1996 across coastal and northern Sahel stations, with a secondary warming shift circa 2010-2015 in inland stations. Cohen’s d effect sizes range from 0.99 to 2.59, confirming large-magnitude warming shifts of +0.5°C to +1.4°C. Precipitation breakpoints are substantially weaker, consistent with high Sahelian rainfall variability. The ML Ensemble method demonstrates superior multi-breakpoint detection capacity and provides continuous probability surfaces rather than binary outcomes, enabling richer uncertainty quantification. These findings carry direct implications for climate adaptation planning, water governance, and territorial resilience strategies across the West African Sahel.