## 4 Discussion  
Global warming is unequivocal, and the frequency and intensity of extreme precipitation are ever-increasing. Understanding the impacts of extreme precipitation events under a warming climate is crucial. A more thorough analysis of the spatiotemporal distribution of seasonal and annual precipitation dynamics and several precipitation indices is critical for effectively managing water resources and associated socioeconomic development. The country has a high spatial variability from east to west (longitudinal), north to south (latitudinal), and topography (elevation) [79; 83]. Ranges of high mountains and low-mid elevated hills, along with dense river networks and sporadic locations of flatland and river valleys, create several microclimatic features and unique precipitation patterns and make highly heterogeneous distribution across the country. [6; 78; 98; 99; 100; 101]. Along with spatial variation, a large seasonal precipitation gradient is seen across the country, resulting in highly temporal variation. Figure 6 shows the temporal fluctuation of country-averaged extreme precipitation indices based on GP stations and all IMERG grids. Since it is not a one-to-one comparison, we evaluated satellite estimates at the precipitation observation station using the point-to-pixel approach (Figure A1 in Appendix A). Overall, the country-averaged temporal precipitation variation, represented by the 25 selected grid locations of selected GPs, is in correspondence with the one computed using the entire IMERG grids. The temporal variation of most precipitation indices confirms the similar temporal fluctuation between satellite estimates and ground-based stations except for IMERG_HQ. However, IMERG_Cal stands out among MERG datasets for achieving a congruous pattern with GP observation, which agrees with the importance of gauge calibration on multi-satellite estimates [57; 59]. Specifically, the frequency of wet days (R1) is well captured by IMERG_Cal, IMERG_Uncal, and IMERG_IR, indicating that precipitation detection is well captured. Figure A2 in Appendix A section shows the performance metrics of precipitation detection based on POD, CSI, false alarm ratio (FAR), and frequency bias index (FBI). For instance, POD ranges from 0.4 to 0.76 (0.32 to 0.77), with an average value of 0.63 (0.6), respectively, for IMERG_Cal (IMERG_Uncal). These obtained ranges of POD and CSI indicate good detection. However, these estimates have sizeable false alarms, indicating substantial false precipitation detections during non-precipitation days depicted by GP but FAR ranges in a similar range for all IMERG datasets. For example, its value ranges from 0.2 to 0.73, with an average of  
0.41 for IMERG_Cal. Likewise, other indices, such as IMERG datasets underestimate CWD, PRCPTOI, monsoon, R20, and R50 throughout the study period. Though the frequency of wet days is well represented, the simple daily precipitation intensity index is highly underestimated because of the underestimation of total annual precipitation. IMERG_Uncal shows slightly overestimated extreme precipitation for many years, such as R100, r95p, r99p, RX1 day, RX3 day, RX5 day, and RX7 day (i.e., heavy precipitation events), which is in line with previous studies [57; 66; 103]. Further, Figure 7 shows the spatial distribution of the monotonic trend of precipitation indices at each grid based on IMERG_Cal and ground-based observations (Figures S1-S3 in the supplement section for IMERG_Uncal, IMERG_HQ, and IMERG_IR). Again, we could see a mixed pattern of increasing and decreasing trends, where most locations have a falling trend except for consecutive dry and wet spells. Figure 6: Temporal variation of the country-averaged value of 28 precipitation indices based on 25 ground-based precipitation stations (obs) and 1538 grid points of 4 IMERG datasets (Cal, Uncal, HQ, and IR) for the period from 2001 to 2020. Refer to Table 1 and Appendix A Table A1 for the details of the indices and types of IMERG datasets.