## 1 Introduction
Nevertheless, several studies have concluded that new generation SBPs products can represent either the spatial pattern or the overall amount and general characteristics of extreme precipitation events over China [35; 36], Cyprus [37], Austria [38], Africa [39] and India [40]. Besides several global studies, only a few studies have evaluated the SBPs in a topographically challenging region like Nepal. For example, the TRMM precipitation product shows negative bias (underestimation) as compared to gauge observations over the Himalayan region of the country [41]. Similarly, [PERSON] et al. [12] found comparable results for 15 stations across the country. In contrast, [PERSON] and [PERSON], [42] indicated that the TRMM product generally overestimated (positive bias) the precipitation as compared to a gauge-based gridded product (the Asian PrecipitationHighly Resolved Observational Data Integration Towards Evaluation: APHRODITE) over Nepal. In mountainous regions the TRMM (3B43) precipitation product shows reasonable skill, while the GSMaP, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), and the Climate Prediction Center Morphing Method (CMORPH) products showed considerably weaker performances in reproducing gauge-observed precipitation amounts [6]. A study in a high-elevation area (Khumbu Himalayas) of Nepal compared seasonal and diurnal variations of precipitation in TRMM (3B42), PERSIANN, CMORPH, and GSMaP products using hourly gauge observed precipitation [43]. They found that GSMaP performed poorly, while TRMM, PERSIANN and CMORPH had good agreement with rain-gauge data. Recently, [PERSON] et al. [44] evaluated the GPM-era SBP products over different complex terrain areas, including ten stations from Nepal. They found that GSMaP-V07 was better for measuring the orographic precipitation and precipitation amount as compared to IMERG-V06B after the orographic rainfall classification ensemble in the GSMaP algorithm. The authors also noticed the better performance of IMERG-V05B to capture the light and heavy precipitation amount as compared to IMERG-V06B for the evaluated regions. Most of the past studies in Nepal were based on previous-generation satellite products, which showed that errors in SBP estimates were partially related to the rugged topography as their algorithms could not detect orographically-induced precipitation appropriately. Additionally, the local climate and nature of the topography are some of the dominant factors to characterize the uncertainty of SBP products [45; 46; 47; 35]. However, a systematic evaluation of the new-generation SBP products, and a intercomparison between these products, has not yet been performed at the national scale. Thus, in this study, we aimed to comprehensively evaluate four precipitation datasets from the two SBP products, i.e., GPM-era IMERG (V06B) and GSMaP (V07), against 388 gauge observations concerning their spatial and seasonal accuracy over Nepal. Their performances are analyzed for their tendencies and discrepancies depending on the different elevation range, and relative intensities on a daily and monthly timescale from March 2014 to December 2016. Moreover, the accuracy of East to West diversion of monsoon and extreme wet events on these SBP products are also analyzed. The result of this study will help to provide critical scientific references to choose the appropriate product for future scientific research.