Deconstructing the spatiotemporal characteristics of extreme precipitation events from multiple data products during Indian summer monsoon

India The rising frequency of extreme precipitation events (EPEs) alters Earth systems processes and poses growing risks to socio-economic stability, intensified by climate change. This study analyzes the spatiotemporal characteristics of EPEs across the Indian subcontinent during the monsoon season, critical for the region’s water resources and agriculture. Using observational (IMD, APHRODITE), reanalysis (IMDAA, GLDAS, ERA5-Land), satellite (CHIRPS, PERSIANN-CDR), and hybrid (MSWEP) datasets, we assess their ability to reproduce EPE intensity, detectability, timing, trends, and statistical properties. Results identify MSWEP as the most reliable alternative to IMD in data-scarce regions, providing valuable insights for hydrologic studies, climate impact assessments, disaster risk management and enhancing socio-economic resilience. The study reveals that EPE intensity and frequency are highest along India’s western coast and northeast, moderate in central regions, and lowest in arid western and peninsular areas. Wet-to-wet, dry-to-dry, and wet-to-dry transitions follow similar regional patterns. Satellite datasets generally underestimate, while reanalysis datasets overestimate EPE intensities, introducing wet and dry biases in moderate-intensity event frequencies, respectively. In contrast, both datasets report an overestimation of low-intensity event frequencies. MSWEP shows the best performance with the lowest bias and highest detectability, while MSWEP and APHRODITE best preserve spatial patterns of median EPEs. No consistent EPE trend clusters are found. These findings support adaptive hydrologic design and disaster risk mitigation to combat climate change. • Spatiotemporal characterization of extreme precipitation events in eight datasets. • Dry and wet bias in satellite and reanalysis datasets for event intensities. • Over- and underestimation of event frequencies by reanalysis and satellite datasets. • MSWEP, APHRODITE and ERA5-Land are found to be the most suitable datasets. • Lack of consistent spatial clusters of trend across all datasets.