###### Abstract
Precipitation estimates using multiple SPEs provide greater confidence in accuracy and consistency in precipitation magnitude and detection [27]. Multi SPEs based datasets include Multi-Source Weighted-Ensemble Precipitation (MSWEP) [28], Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) [29], the Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN) [30], IR-based PERSIANN [31], PERSIANN-Cloud Classification System (CCS) [32], Climate Prediction Center morphing technique (CMORPH) [33], Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) [34], NRL-Blend satellite rainfall estimates [35; 36] from the Naval Research Laboratory (NRL), GsMap [37; 38], and AIMERG: IMERG calibrated using APHRODITE [39], and many others. In particular, TMPA has been accepted to capture intense tropical cyclone rainfall and correlate with GP observations [40; 41]. Along with extreme precipitation events, TMPA has been successful on various terrain, including most low-latitude areas and large-medium scales, and has been applied for hydrological modeling, soil moisture prediction, fire detection, and agriculture [42; 43]. However, the performance of the TMPA is prejudiced by several factors, such as spatial-temporal scales, regions, and seasons [44]. As an illustration, the under-performance of the TMPA has been reported in rugged and high-elevation areas [45; 46; 47]. [PERSON] et al. [46] compared the individual performance of several SPEs, including TMPA, against GP observations in the Tibetan Plateau and explored that these products tended to overestimate light rainfall and underestimate moderate and heavy rainfall. Moreover, the performance was better in humid regions than in arid regions [48]. A similar overestimation in seasonal performance was reported in Saudi Arabia and Bangladesh [49; 50]. As a progressive step, IMERG was launched in February 2014 [29]. The IMERG product has been evaluated and applied in the majority of fields due to high spatial (0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\), compared to 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) of TMPA) and temporal (30 min) resolution, global coverage, open access, and relative short latency by combining the passive MW sensors from low Earth orbit satellites and geosynchronous Earth orbit IR sensors [51; 52]. Several previous studies have evaluated the performance of new-era IMERG products on global aspects by considering the effect of topography, landscape, and seasonal characteristics. Studies have reported good performance agreement with the gauge and ground-based radar data [16; 43; 53; 54; 55]. Moreover, a few studies in the Asian region demonstrate a wide range of diversity in climate patterns and fluctuating topography [53; 56; 57]. In a nutshell, notable findings of these studies are (i) IMERG attained substantial improvement in capturing spatiotemporal precipitation variability and very light to light rainfall events along with low bias and improved correlation coefficient with GP stations across the climatic and geographic conditions, and (ii) IMERG demonstrates better improvement and yields better results of false alarm ratio (FAR), probability of detection (POD), and critical success index (CSI). IMERG provides three products: IMERG early run (ER), IMERG late run (LR), and IMERG final run (FR). Various comparative assessments of these products have been conducted, e.g., central Himalayan country Nepal [58], a temperate broad-leaved forest climate in Austria [59], the subtropical monsoon climate in Pearl River Basin of China [60], the tropical desert climate in Saudi Arabia [56], southwest Pacific region [61], and the temperate monsoon climate in Huaihe River Basin of China [62]. [PERSON] et al. [58] evaluated the performance of IMERG's three runs (ER, LR, and FR) over Nepal with reference to 231 GP stations, and they reported IMERG FR to be slightly better than the other two. [PERSON] et al.