A Graph Neural Network Framework for Characterizing Rainfall Variability Regimes across India
TL;DR
Results reveal a previously unreported coupling: regions with higher rainfall volumes are also the most temporally repeatable year-to-year, demonstrating an emergent, spatially coherent structure.
Abstract
The Indian Summer Monsoon shows significant spatial variation. While prior work primarily focused on forecasting rainfall amounts, little attention has been given to how consistently a location's seasonal rainfall trajectory repeats from year to year. We introduce a graph-based machine learning framework to classify locations across India by this inter-annual consistency. Using 2001 to 2022 GSMaP ISRO data (excluding 2012), we constructed graphs for 29,026 grid points where nodes represent individual years and edges denote cosine similarity. A Graph Convolutional Network classified locations as either consistent or erratic with 96.8% accuracy. Applied to the Indian landmass, the model successfully identified the Western Ghats, Northeast India, and parts of central India as consistent regions. This classification was rigorously validated through statistical testing and temporal stability analysis, showing 93.6% agreement across two independent timeframes. Crucially, the results reveal a previously unreported coupling: regions with higher rainfall volumes are also the most temporally repeatable year-to-year, demonstrating an emergent, spatially coherent structure.