Comparative Assessment of Random Forest and Linear Regression for Predicting South Asian Aridity Driven by Tropical Ocean Signals

The South Asian monsoon sustains nearly one-quarter of the global population, yet the combined effects of water supply and atmospheric evaporative demand on regional aridity remain poorly integrated in long-term assessments. This study characterizes spatial and temporal aridity dynamics across the South Asian Monsoon region from 1901 to 2024 using the UNEP Aridity Index, offering a comprehensive view of hydroclimatic stress beyond precipitation alone. We integrate gridded climate observations with sea surface temperature records through Empirical Orthogonal Function decomposition and an interpretable machine learning framework, comparing linear regression against Random Forest and Hybrid models trained on historical data and validated independently. Our analysis reveals pronounced warming, spatially heterogeneous drying concentrated in northwestern regions, and a robust ENSO-aridity teleconnection modulated by the Indian Ocean Dipole. Machine learning models demonstrate superior skill in capturing nonlinear, threshold-dependent responses, yet their performance varies substantially across aridity zones, with linear approaches failing entirely in humid regions while hybrid frameworks excel in drylands. Critically, after removing long-term trends, interannual predictability persists most strongly in hyper-arid and semi-arid zones, where ENSO and IOD signals remain detectable, but declines elsewhere. SHapley Additive exPlanations identify Niño3.4 as the dominant oceanic predictor, with extreme El Niño events disproportionately intensifying aridity. These findings demonstrate that tropical ocean signals alone explain only modest year-to-year variability in the regional mean, with predictability concentrated in specific dryland zones. This work provides a diagnostic foundation for drought early-warning systems while emphasizing the need to incorporate local terrestrial processes and long-term trends for operational forecasting in one of the world’s most climate-vulnerable regions.