Assessing the Shifting Drivers of Rainfall Extremes in Peninsular India: From Remote Teleconnections to Regional Thermodynamics

The stationarity of rainfall extremes is increasingly challenged by a changing climate, necessitating a deeper understanding of both remote and regional atmospheric drivers. While traditional risk assessments for India often rely on global climate indices like the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), these one-dimensional approaches often struggle with covariate multicollinearity and fail to capture interacting physical processes. This study explores the Principal Component Analysis (PCA) with wavelet coherence to evaluate the influence of nine climate indices on extreme monthly rainfall across peninsular India (1901–2021). By transforming correlated predictors into orthogonal joint modes, we found that while the primary modes of global climate variability account for nearly half of the total variance, their direct coherence with localized rainfall extremes remains weak and intermittent. In contrast, principal components dominated by regional thermodynamic indicators (specifically Integrated Vapor Transport (IVT) and local temperature anomalies) demonstrated the most persistent and statistically significant coherence, affecting over 80% of the study area. Furthermore, cross-correlation analysis revealed that while ENSO exhibits a 2–3 month lag, regional variables exert a contemporaneous influence on extreme events. Our findings suggest that the governance of rainfall extremes is shifting toward regional-scale processes. Consequently, we argue that for the development of non-stationary extreme value models, local covariates should be prioritized over remote teleconnections. In practical applications, high-resolution products from regional climate models, offer a more physically representative and contemporaneous basis for capturing the drivers of extreme events. This shift in covariate selection has critical implications for improving the accuracy of hydrological hazard assessments and infrastructure design in a non-stationary world.