###### Abstract  
Accurate accounting of spatiotemporal variability of precipitation is essential for understanding the changing climate. Among the available precipitation estimates, the Global Precipitation Measurement (GPM) is an international satellite network providing advanced global precipitation estimates. The integrated multi-satellite retrievals for GPM (IMERG) algorithm combines information from the GPM satellite constellation to estimate precipitation and yields a better performance in detecting precipitation events and spatial resolution. Here, we used twenty years (2001-2020) of IMERG Final data over the entire Nepal to analyze the spatial and temporal distribution of precipitation. This study evaluates the dynamic characteristics of the precipitation amounts, intensities, frequencies, and other relevant data across Nepal, using four IMERG datasets: (i) microwave only, (ii) infrared only, (iii) multi satellites gauge uncalibrated, and (iv) multi satellites gauge calibrated. A total of 28 precipitation indices was computed: threshold-based counts, consecutive days, precipitation amounts and extremes, precipitation intensity, percentile-based extremities, proportion-based indices, and additional seasonal indices. Results show that all four IMERG datasets are promising in capturing spatial details. The frequency of wet days corresponds with ground-based precipitation. Still, most indices, including consecutive wet days, annual and monsoon precipitation, and days when precipitation equaled or exceeded 20 and 50 mm, were substantially underestimated. In addition, the microwave-only dataset highly underestimated the precipitation amount. Notably, a substantial proportion of false alarms is a problem for all four IMERG datasets. Moreover, our results demonstrate that the IMERG uncalibrated dataset tends to overestimate precipitation during heavy precipitation events. These advantages and shortcomings of IMERG datasets over the rugged terrain of Nepal can provide useful feedback for sensor and algorithm developers to overcome limitations and improve retrieval algorithms. The study findings are helpful to the broader data users and practitioners for effective water decision applications. 20221 November 20221 November 2022  
\(\sim\)7% per degree of warming, indicating a higher chance of heavier precipitation [8; 9; 10]. Such extreme precipitation events would increase the risk of flash floods and landslides, threatening socioeconomic infrastructures [11; 12; 13; 14]. Therefore, accurate accounting of the spatiotemporal precipitation variability is the starting point for filling the \"real holes\" in understanding the precipitation's nexus with coherent and recurrent climatic [15; 16; 17]. Various methods of precipitation measurements are prevalent, including ground-based precipitation (GP) stations, weather radars, and satellite-based precipitation estimates (SPEs). GP observation is the most traditional and accurate method of precipitation measurement [18]. However, it is a challenging task to capture the spatiotemporal dynamics for larger regions, especially the estimation of the extreme across remote locations, which becomes worse with instrumental and measurement error [19; 20] and decreasing number of GP stations across the world with the lack of a budget for effective operation, maintenance [21; 22; 23]. Complementary to GP observations, space-borne sensors and satellite-based retrieval algorithms provide great spatiotemporal details with global coverage, establishing them as a robust complement for water system modeling globally, especially in data-scar regions [24; 25]. Reanalysis products such as ERA-Interim Reanalysis of European Centre for Medium-Range Weather Forecasts (ECMWF), Japanese 55-year Reanalysis (JRA-55), National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis-1 (NCEP/NCAR-1), and High Asia Refined analysis (HAR) [26] are based on in-situ data, numerical models or/and SPEs.