Integrating IMDAA Regional Reanalysis and Machine Learning for Enhanced Detection of Extreme Precipitation Over Complex Himalayan Terrain

Abstract

The ecologically fragile Himalayan region faces escalating vulnerability to extreme precipitation events driven by orographic-atmospheric interactions. However, forecasting these events remains a formidable challenge, as traditional global models often fail to capture mesoscale convective extremes due to coarse spatial resolutions. North India serves as a crucial agricultural “breadbasket,” yet its hydrological integrity is increasingly compromised by elevation-dependent warming. Accurate detection of these shifting precipitation regimes is essential for developing a foundational diagnostic framework for decision-support systems and mitigating disasters like cloudbursts and flash floods. This study evaluates the high-resolution (12 km) Indian Monsoon Data Assimilation and Analysis reanalysis dataset (1979–2022) combined with machine learning classifiers. These models were designed to categorize daily accumulated rainfall into percentile-based groups - specifically moderate, heavy, and extreme - for targeted detection of high-impact events. Beyond a statistically significant increasing trend of + 3.0 mm/decade in annual precipitation across the Himalayan foothills, the study found that ensemble based learning (RF) demonstrated clear superiority over geometric classifiers (SVM) in achieving significant overall accuracy of 81.6% in predicting tail-end distributions. RF achieved a precision of 0.80 for 'Extreme' events with a high degree of specificity, suggesting its potential for reducing false-alarm rates in complex orographic zones. These findings establish the superiority of ensemble-based learning over geometric classifiers for meteorological applications in complex terrain. The Random Forest based framework offers a reliable, cost-effective tool for operational forecasting, bridging the gap between coarse global models and local observational scarcity to support disaster mitigation strategies in North India.