Cutting-edge trends in drought prediction and mitigation using ensemble learning and augmented XAI: a meteorological study

Drought affects more than 60% of India's population, reducing agricultural productivity, straining water resources, and threatening socioeconomic stability. This study proposes an interpretable one-month-ahead meteorological drought prediction framework by integrating class-weighted ensemble machine learning with SHAP and LIME explainable artificial intelligence (XAI). Historical meteorological observations (1972–2022) obtained from the Zenodo climate database were analyzed for 49 districts across five drought-prone Indian states (Bihar, Maharashtra, Gujarat, Andhra Pradesh, and Karnataka). After generating lagged meteorological features (t − 1 to t − 6) and addressing severe class imbalance (7.07% drought events), multiple machine learning classifiers were evaluated. The class-weighted XGBoost model (scale_pos_weight ≈ 11.90, without SMOTE) achieved the best overall performance on the independent 2016–2022 test dataset, with an accuracy of 95.7%, macro precision of 84.7%, macro recall of 81.0%, macro F1-score of 82.7%, and a PR-AUC of 0.647 on the highly imbalanced test set (outperforming both unweighted training and SMOTE-based approaches). SHAP and LIME consistently identified lagged Standardized Precipitation Index (SPI), lagged precipitation, vapour pressure, and wet-day frequency as the most influential drivers of drought occurrence. Exploratory temporal analysis further revealed declining wet-day frequency in Karnataka, increasing vapour pressure in Andhra Pradesh, and substantial climatic variability across the study regions. The proposed framework combines accurate drought prediction with transparent model interpretation, providing a practical decision-support tool for district-level drought early warning, water resource management, and climate-resilient agricultural planning in India.