Understanding ENSO-Driven Climate Extremes over India: Insights from Composite and Non-Stationary Generalized Extreme Value Analyses

The El Niño–Southern Oscillation (ENSO) was the dominant mode of interannual climate variability and significantly influenced temperature and precipitation extremes over India. This study investigated ENSO influences on winter temperature and Indian Summer Monsoon Rainfall (ISMR) extremes using ETCCDI climate extreme indices. Composite analyses revealed distinct responses of winter temperature and monsoon precipitation extremes to ENSO. During winter, El Niño suppressed cold extremes by increasing nighttime temperatures, reducing cold-event frequency, and shortening cold-spell duration, whereas La Niña enhanced their occurrence, intensity, and persistence, particularly during January and February. To quantify ENSO influences, non-stationary statistical models incorporating the Niño-3.4 index were applied. GEV analysis revealed predominantly positive Niño-3.4 coefficients for TNn and TNx during January and February, indicating warmer nighttime extremes during El Niño. Negative Binomial-based analysis further indicated a widespread reduction in cold-night frequency (TN10p) and cold-spell duration (CSDI) during El Niño. For precipitation extremes, GEV analysis showed predominantly negative Niño-3.4 coefficients for Rx1day, Rx5day, and PRCPTOT over large parts of India, while positive coefficients for CDD indicated enhanced dry-spell persistence during El Niño. During the monsoon, El Niño was associated with reduced precipitation extremes across most of India, while enhanced rainfall was largely confined to southern Peninsular India. In contrast, La Niña produced a heterogeneous response, with enhanced rainfall over southern Peninsular India and the Indo-Gangetic Plain but reductions across parts of eastern, western, and northern India. Overall, the study provided an integrated assessment of ENSO-driven climate extremes over India, highlighting contrasting seasonal and regional responses in temperature and precipitation extremes.