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This study aims to assess the accuracy of two satellite‐based precipitation products (SBPPs), that is, Tropical Rainfall Measurement Mission (TRMM)‐based Multi‐satellite Precipitation Analysis (TMPA) and its upgraded version Integrated Multi‐Satellite Retrievals for Global Precipitation Measurement (IMERG), in capturing spatial and temporal variation of precipitation and their application for extreme events (high‐intensity precipitation and drought). They were evaluated against 142‐gauge stations from Nepal during 2001–2018. The results show that, in general, both SBPPs show the overall characteristics of precipitation patterns, although underestimated the mean annual precipitation during the study period. It was also noted that IMERG product yields better performance to detect precipitation events (probability of detection) and no‐precipitation events (false alarm ratio) than TMPA. Based on four different extreme precipitation indices: heavy precipitation events (R10mm), extreme precipitation events (R25mm), five consecutive dry days (CDD), and five consecutive wet days (CWD), it was observed that the SBPPs underestimated the frequency of R25mm and CDD spells while overestimated R10mm and CWD spells. Additionally, both SBPPs exhibited considerable capabilities in capturing the drought events during the study period. Overall, the drought event, bias, and frequency show that the IMERG product has slightly better capabilities to capture drought than the TMPA product. In general, IMERG was found to be superior at a daily timescale, while TMPA shows consistent performance on a monthly scale during the study period. Furthermore, there is still space for further improvement of IMERG rainfall retrieval algorithms. Key Points: TMPA and IMERG precipitation products are evaluated against gauge observation from Nepal HimalayasIMERG product performs better for daily timescale, while TMPA shows the best performance at monthly timescaleIMERG product is found to be a good alternative to evaluate extreme precipitation events (both high‐intensity related and drought) [ABSTRACT FROM AUTHOR]

Summary Soil moisture is a key factor in energy and water cycles. Many satellite missions have been planned and implemented for retrieving soil moisture globally. Because the spatial representativeness of a point-scale soil moisture station is rather limited, a station network needs setting up for scale-matching validation of satellite-based soil moisture products. Even so, an upscaling procedure is needed to upscale these station soil moisture values into area-wide one. However, such a procedure itself introduces uncertainties into the upscaled soil moisture. In this study, four upscaling methods (simple average, block kriging, model-based, and apparent-thermal-inertia-based) are inter-compared according to their performance stability for evaluation of soil moisture estimated by assimilating microwave signals into a land surface model. It is found that the performance of the model-based upscaling approach is the most unstable because model simulations are full of uncertainties for representing spatial variability of soil moisture. The block kriging upscaling method performs not worse than the simple averaging approach; the former may generate more representative soil moisture if the range of the soil moisture semivariogram used in the block kriging is comparable to the extent of a satellite footprint. The apparent-thermal-inertia-based upscaling is the most stable one, which has been developed with the aid of high-resolution satellite thermal data. All analyses indicate that choosing a suitable upscaling approach is important for the effective evaluation of satellite-based soil moisture. Otherwise, uncertainties hiding in the upscaling method will be incorrectly attributed to errors in satellite products, undermining our confidence in implementing them into practice. [ABSTRACT FROM AUTHOR]