## 1 Introduction  
Precipitation is a crucial component of the Earth's water cycle, associated with atmospheric circulation in weather and climate studies [1]. Furthermore, precipitation is an essential determinant of a variety of socio-economic practices, including hydrological modeling [2, 3], hydro-electric power generation [4], drought detection and monitoring [5], flood prediction and modeling [6], and food security [7]. Consequently, the availability of accurate and reliable precipitation data is of significant importance in supporting the successful execution of these practices. Conventionally, precipitation data were only collected using in situ instruments, such as rain gauges, synoptic stations, and ground-based radar. Although these observations, as the most intuitive method, provide the most accurate precipitation data due to their direct physical measurement, they include several limitations that may hamper our understanding of various climate events [8]. In particular, in situ measurements suffer from a sparse and uneven distribution, incomplete areal coverage, and recording sequence discontinuities [9; 10]. The aforementioned limitations could result in an inadequate spatial and temporal representation of precipitation patterns, especially in remote regions and areas with a complex terrain [11; 12]. Additionally, the high cost associated with installing and consistently maintaining accurate ground stations makes it almost impractical to establish a very high-density network with a suitable spatial distribution. Accordingly, the utility of other technologies and datasets that could potentially address these limitations should be on the agenda. More recently, different Gridded Precipitation Products (GPPs) have emerged, satisfying the requirements and resolving previous limitations that could suitably support a variety of applications. These GPPs, covering regional and global scales with consistent and various spatial and temporal resolutions, have been developed from (1) in situ measurements, (2) numerical models, (3) satellite data, and (4) a combination of different sources [13]. Among these data sources, the advancement of infrared and microwave sensors onboard satellites has significantly contributed toward retrieving precipitation estimates with homogenous spatial and temporal characteristics. For instance, the Tropical Rainfall-Measuring Mission Multi-satellite Precipitation Analysis (TMPA) [14], the Global Satellite Mapping of Precipitation (GSMaP) [15], and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) [16] are among the widely used satellite-based GPPs. Additionally, the combination of various data sources, such as the integration of satellite data with in situ measurements (i.e., for calibration and adjustment) and data assimilation through the atmospheric reanalysis/modeling of climate data, generally have enhanced the performance of the GPPs [17; 18; 19]. Due to their consistent spatial and temporal resolutions, these GPPs have been employed globally by a range of researchers for a wide variety of applications [20; 21; 22]. For instance, GPPs were employed for drought monitoring [23; 24], flood prediction and modeling [6; 25], water resource management [26], stream-flow estimation [27; 28], and trend analysis [29; 30]. However, the discrepancies between different GPPs, affected by different sensor types, algorithm principles, assimilation methods, and input data sources, necessitate the performance evaluation [9; 31]. Performance evaluation is a prerequisite to guarantee success in different studies as it notably depends on the accuracy of GPPs [9]. Therefore, many studies have been carried out to investigate the accuracy of different GPPs in different time scales using in situ observations [6; 13; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42]. For instance, [PERSON] et al. [35] examined the performance of ten satellite-based and reanalysis GPPs throughout China between 2000 and 2018 on different time scales.