Decoding of long-term spatio-temporal precipitation dynamics over India: Insights from 115 years of meteorological data

Understanding precipitation variability is crucial for assessing climate change impacts on ecosystems, agriculture, and water resources. This study examines the past 115 years of long-term rainfall data across 34 meteorological subdivisions in India using statistical techniques, including the double exponential model (DEM), Mann-Kendall test, and Generalized Extreme Value (GEV) analysis. The key findings are: (1) A significant shift in rainfall patterns occurred post-1945 and 1990, confirmed by Pettitt’s test and the Mann-Whitney U test (p = 0.0025), with α values declining from 2.2 to 1.5 cm and β values fluctuating between 100 and 300 cm, indicating increased rainfall intensity; (2) Extreme rainfall events (≥90 cm/day) are concentrated in coastal regions (Kerala, Karnataka, Maharashtra) and northeastern states (Assam, Manipur, Shillong), as validated by GEV-based return period analysis; (3) Monsoon season variability accounts for 55–65 % of rainfall fluctuations, driven by short-term oscillations such as oscillations El Niño-Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD), while long-term climatic trends contribute 35–45 % of the variance; (4) Sen’s slope analysis reveals increasing monsoon rainfall trends in Jammu & Kashmir, Punjab, Rajasthan, and Gujarat, whereas parts of Maharashtra and northeastern India exhibit a decline. These findings provide critical insights for water resource management, disaster preparedness, and climate adaptation strategies, supporting evidence-based policymaking for sustainable development.