Abstract

Pre-monsoon (March-May; MAM) rainfall over Telangana is highly variable, yet district-scale evidence on its variability, extremes, and near-term change remains limited. This study combines India Meteorological Department gridded rainfall observations (1951-2024), bias-corrected NEX-GDDP-CMIP6 simulations, and machine-learning (ML) post-processing to examine MAM rainfall in six contrasting districts: Adilabad, Nizamabad, Karimnagar, Hyderabad, Mahabubnagar, and Khammam. Observations show strong spatial heterogeneity, with coefficients of variation from 60.2% in Hyderabad to 83.0% in Khammam. Trend decomposition indicates that recent changes are more closely linked to increasing rainfall intensity than wet-day frequency; the strongest seasonal trends occur in Nizamabad and Khammam (about 0.32 mm yr-1), although statistical significance is spatially limited. Twenty CMIP6 models were evaluated using a composite performance index based on correlation, normalized error, variability ratio, and bias. The five best-performing models, CMCC-ESM2, ACCESS-ESM1-5, TaiESM1, MRI-ESM2-0, and INM-CM5-0, were retained for ML post-processing. Random Forest, XGBoost, and LSTM showed comparable skill during independent testing, but LSTM produced more stable district-wise correlations while still underestimating peak rainfall years. Under SSP5-8.5, LSTM-based projections for 2031-2050 indicate changes from -5.2% in Nizamabad to +36.4% in Adilabad relative to 1981-2010, supporting localized interpretation rather than broad generalization for regional climate-risk assessment and adaptation planning practice.

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