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Highlights • Comparison of IMD and TRMM-TMPA, CMORPH, PERSIANN, and MSWEP precipitation data. • Mann–Kendall (MK), modified Mann–Kendall (MMK), and Sen's Slope for trend analysis. • PERSIANN precipitation data has shown better trend result with the IMD data. Abstract This study compares the precipitation trend from the gridded rain gauge data collected by India Meteorological Department (IMD) and Multisatellite High Resolution Precipitation Products (HRPPs) including Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) version 7, Climate Prediction Center Morphing (CMORPH) version 1.0, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1 for the river basins of India. The IMD and HRPPs are of the same spatial resolution (0.25° × 0.25°) and extend from 1998 to 2015. The gridded precipitation datasets are compared for 25 river basins of India. TRMM, CMORPH, PERSIANN, and MSWEP datasets accuracy for the river basins are assessed by comparison with IMD using root mean square error (RMSE) and correlation coefficient (CC) methods. The Mann–Kendall (MK) and modified Mann–Kendall (MMK) tests are applied for analyzing the data trend, and the change is detected by Sen's Slope using all datasets for annual and seasonal time periods. Variation in precipitation is high (>20%) in the northern part of India in all datasets. All these basins located at the elevations above 2000 m. The annual and monsoon trend pattern for TMPA, CMORPH, PERSIANN, and MSWEP matched with IMD data in the north, northwest, and central part in 1–2, 22–25, and parts of 3, 12, and 21 river basins (1998–2015). The calculated results implied that the TMPA precipitation product (in terms of accuracy) and PERSIANN (in terms of annual and monsoon trend) show a better agreement with IMD and they can be used in climate studies and hydrological simulation in locations/river basins where the number of rain gauges is not adequate to quantify the spatial variability of precipitation. [ABSTRACT FROM AUTHOR]