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The Indian summer monsoon (ISM) is a complex and multiscale interacting climate system, which is responsible for major contribution in India's annual rainfall. Understanding the spatial and temporal variability of ISM rainfall is critical for managing water resources, which directly impact and regulate the functioning of India's socio-economic conditions and subsequently, sustenance of over a billion people. This study evaluates the suitability of various gridded precipitation data products with different spatiotemporal resolutions, essential requirement for hydrologic modeling, disaster mitigation, irrigation allocation and agricultural application. Hence, we evaluate the performance of seven gridded datasets in generating time-matched characteristic event occurrences and their respective magnitudes using gauge-based Indian Meteorological Department (IMD) gridded data as reference. We observe that reanalysis datasets underperform compared to satellite and hybrid products in identifying both normal and extreme precipitation events. We develop a performance measure, called 'rank score' that considers deviations from IMD data in magnitude, statistical moments, and rain event detectability for a robust assessment and identifying best-suited dataset. Results indicate that APHRODITE, MSWEP, and CHIRPS (in descending order) are the most suitable data products across India. Additionally, region-specific evaluations provide valuable insights into the applicability of these datasets in different climatic and homogeneous rainfall zones. [ABSTRACT FROM AUTHOR]

Abstract The Indian summer monsoon (ISM) is a complex and multiscale interacting climate system, which is responsible for major contribution in India’s annual rainfall. Understanding the spatial and temporal variability of ISM rainfall is critical for managing water resources, which directly impact and regulate the functioning of India’s socio-economic conditions and subsequently, sustenance of over a billion people. This study evaluates the suitability of various gridded precipitation data products with different spatiotemporal resolutions, essential requirement for hydrologic modeling, disaster mitigation, irrigation allocation and agricultural application. Hence, we evaluate the performance of seven gridded datasets in generating time-matched characteristic event occurrences and their respective magnitudes using gauge-based Indian Meteorological Department (IMD) gridded data as reference. We observe that reanalysis datasets underperform compared to satellite and hybrid products in identifying both normal and extreme precipitation events. We develop a performance measure, called ‘rank score’ that considers deviations from IMD data in magnitude, statistical moments, and rain event detectability for a robust assessment and identifying best-suited dataset. Results indicate that APHRODITE, MSWEP, and CHIRPS (in descending order) are the most suitable data products across India. Additionally, region-specific evaluations provide valuable insights into the applicability of these datasets in different climatic and homogeneous rainfall zones.