## 4 Summary and Conclusions  
India receives about 80% of its annual rainfall from the southwest or summer monsoon spanning from June to September, which exhibits substantial spatiotemporal variability. In addition, diverse topography and a fairly good rain gauge network make India a good test-bed to evaluate any satellite-based precipitation product. However, very few studies dealt with the evaluation of IMERG products for the southwest monsoon season over the country. In this study, near-real-time (e.g., V6 of IMERG-E and IMERG-L) and research products (IMERG-F V6 and V5) of IMERG were comprehensively evaluated against the IMD rain-gauge-based dataset over India at a daily scale for the southwest monsoon period. The evaluation was also carried out for pre-GPM and GPM periods. The spatial distributions of different error metrics across the country showed similar performance by both IMERG-E and IMERG-L estimates in precipitation estimation with marginally better performance by IMERG-L over IMERG-E. However, near-real-time products had rather larger errors than IMERG-F V6 estimates. IMERG-F V6 showed distinct bias patterns from IMERG-E and IMERG-L estimates over the west coast associated with complex terrain and precipitation processes. Bias in all-India daily mean rainfall in the near-real-time IMERG products was about 2-3 times larger than the research product. However, there was no considerable change in error metrics in IMERG-F V6 observed compared to the IMERG-F V5 product. Both near-real-time and research products showed similar characteristics in the detection of rainy days. IMERG-F V6 exhibited an overall better performance in precipitation estimation and detection of rainy days during the GPM period (2014-2017) than the pre-GPM period (2010-2013). Better performance of IMERG during the GPM period than during the pre-GPM period might be due to the availability of the DPR after the launch of the GPM Core Observatory, which provides better calibration than the TRMM-PR for IMERG estimates. Furthermore, IMERG products were evaluated at all-India and sub-regional scales for different precipitation intensity intervals. The contributions of different rainfall intensity intervals to the total monsoon rainfall were captured well by the IMERG V6 estimates (e.g., IMERG-E, IMERG-L, and IMERG-F), but they underestimated the frequency of light to very heavy rainfall intensities over all-India, central India, and west coast regions. Additionally, results indicated that IMERG V6 products under-detected and overestimated light rainfall intensity, which needs to be improved in the next release. Results of this study clearly revealed that the error characteristics of IMERG products differ with region, topography, precipitation process, and product release version.The results of this study would be useful for both end-users and algorithm developers. Nevertheless, there is a  
Figure 12: Bias and root mean square errors in the IMERG version 6 estimates as compared to the IMD rain-gauge-based data for 2014–2017 over all-India, central India, and west coast for different rainfall intensity intervals. need for an extensive evaluation of IMERG products at a sub-daily scale using radar and automatic weather station datasets over India in order to assess their capabilities in the diurnal cycle representation. The integration of ground-based weather radars and surface parameters such as soil moisture and terrain elevation in the multi-satellite precipitation product would further enhance its accuracy for hydrological applications. Conceptualization, methodology, formal analysis, writing--original draft preparation, S.P. ; writing--review and editing, supervision, J.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The APC was funded by the Divecha Centre for Climate Change.