###### Abstract  
Precipitation is one of the integral components of the global hydrological cycle. Accurate estimation of precipitation is vital for numerous applications ranging from hydrology to climatology. Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, the Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation product was released. The IMERG provides global precipitation estimates at finer spatiotemporal resolution (e.g., 0.1\({}^{\circ}\)/half-hourly) and has shown to be better than other contemporary multi-satellite precipitation products over most parts of the globe. In this study, near-real-time and research products of IMERG have been extensively evaluated against a daily rain-gauge-based precipitation dataset over India for the southwest monsoon period. In addition, the current version 6 of the IMERG research product or Final Run (IMERG-F V6) has been compared with its predecessor, version 5, and error characteristics of IMERG-F V6 for pre-GPM and GPM periods have been assessed. The spatial distributions of different error metrics over the country show that both near-real-time IMERG products (e.g., Early and Late Runs) have similar error characteristics in precipitation estimation. However, near-real-time products have larger errors than IMERG-F V6, as expected. Bias in all-India daily mean rainfall in the near-real-time IMERG products is about 3-4 times larger than research product. Both V5 and V6 IMERG-F estimates show similar error characteristics in daily precipitation estimation over the country. Similarly, both near-real-time and research products show similar characteristics in the detection of rainy days. However, IMERG-F V6 exhibits better performance in precipitation estimation and detection of rainy days during the GPM period (2014-2017) than the pre-GPM period (2010-2013). The contribution of different rainfall intensity intervals to total monsoon rainfall is captured well by the IMERG estimates. Furthermore, results reveal that IMERG estimates under-detect and overestimate light rainfall intensity of 2.5-7.5 mm day\({}^{-1}\), which needs to be improved in the next release. The results of this study would be beneficial for end-users to integrate this multi-satellite product in any specific application. multi-satellite precipitation estimate; Global Precipitation Measurement mission; southwest monsoon; rain gauge; error characteristics +
Footnote †: journal: 15 September 2021  
Several global or quasi-global multi-satellite precipitation products were developed and made available to users during the TRMM-era [5; 6]. These precipitation products take relative advantages of the passive microwave imagers onboard the low-Earth orbiting satellites and infrared sensors onboard the geostationary satellites. In addition, these multi-satellite precipitation products provide precipitation information at uniform spatial and temporal scales even over the regions where rain gauge observations are unavailable or meager [7]. Some of the popular TRMM-era multi-satellite precipitation products are TRMM Multisatellite Precipitation Analysis (TMPA [8]), Climate Prediction Centre Morphing (CMORPH [9]), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN [10]), and Global Satellite Mapping of Precipitation (GSMaP [11]). These multi-satellite precipitation products have rather large uncertainties over several regions of the globe at multiple timescales [6]. However, among the TRMM-era multi-satellite precipitation products, TMPA was generally shown to be superior to other products at global and regional scales [12; 13]. The TRMM satellite was decommissioned in April 2015, after 17 years of uninterrupted service. In order to continue the objectives of the TRMM satellite with some further advancement, the Global Precipitation Measurement (GPM) Core Observatory was launched in February 2014.