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 (20012020) 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.