## 5 Conclusions  
Precipitation information is crucial in the global, regional, and local water cycle. Though GP stations are considered ground truth, they suffer from low spatial density and sizeable temporal gaps, particularly in remote areas, including high mountains. SPEs, in the form of gridded data, inform spatially seamless precipitation distribution. This study evaluated the spatiotemporal variability of precipitation using several precipitation indices across Nepal. These indices capture information related to seasonal and annual totals, frequency and intensity of extreme precipitation, and the maximum length of dry and wet spells. Normalized indices (percentage contributions) and extra seasonal indices (Monsoon CDD and Winter CWD) provide spatially comparable and temporally local precipitation information. Though these indices are adapted from popular ETCCDI and ET-SCI, they have additional benefits in understanding the spatiotemporal distribution of precipitation. With a minor modification, these indices can be applied to other regions to capture local and regional insights. Across Nepal, based on 25 GP stations, we observed that IMERG datasets showed good detection efficiency. Nevertheless, there are also sizable false alarms, which is a serious concern. Importantly, the spatial distribution of several precipitation indices shows a convincing variability, which captures seasonal monsoonal variability across the country. Based on the annual average values of PRCPTOT, the country's southern region showed a high-precipitation pocket, missing three local pockets of high-precipitation regions (>3500 mm in a year), which are depicted by previous studies. Therefore, local high-precipitation areas are still not tracked by the IMERG datasets. In addition, upon looking at heavy to very heavy precipitation event-related indices, most of the indices clearly showed the foothills of the country as the highest extreme precipitation areas, supported by GP station observations. This study finds that seasonal precipitation has location-specific rising and falling trends, but extreme precipitation indices have sporadic pockets, indicating the spatial behavior of extreme precipitation is highly local. Among four IMERG datasets, IMERG_Cal is best-suited for further hydrological and water resources implications. IMERG_Uncal is equally applicable, but the uncalibrated version generally shows higher precipitation totals during extreme events. Interannual variations of IMERG_cal and IMERG_uncal are congruous to each other. IMERG_IR slightly underestimates most occasions. IMERG_HQ underestimates substantially. As all IMERG products captured wet days accurately, a proper blending of all these products using several statistical and machine learning approaches could be explored to estimate absolute precipitation values for future hydrologic and water resources applications. A take-home message from this study is that although SPEs provide spatial details irrespective of location, the accuracy and uncertainty of these products vary differently from location to location. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14235954/s1](https://www.mdpi.com/article/10.3390/rs14235954/s1)]([https://www.mdpi.com/article/10.3390/rs14235954/s1](https://www.mdpi.com/article/10.3390/rs14235954/s1)), Figure S1: the same as in Figure 7 but for IMERG_Uncal; Figure S2: the same as in Figure 7 but for IMERG_HQ; Figure S3: the same as in Figure 7 but for IMERG_IR. Conceptualization, R.T., S.S. and B.A. ; methodology, R.T.; software, R.T.; validation, R.T. formal analysis, R.T.; investigation, R.T.; resources, R.T. and S.S.; data curation, R.T. and S.S.; writing--original draft preparation, R.T. and S.S.; writing--review and editing, R.T., S.S. and B.A. ; visualization, R.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding.