## 1 Introduction  
Accurate precipitation data are essential for numerous theoretical and practical applications, be it for water balance calculations, flood warnings, drought monitoring, or water resource management [1; 2; 3]. When properly installed and maintained, rain gauge observations provide accurate point-based precipitation measurements [4; 5]. However, in the case of low coverage density, they are poorly adapted to deal with the high spatiotemporal heterogeneity in precipitation. The latter can result in large errors when rain gauge data are interpolated to larger scales, particularly in mountainous areas with complex terrain [6; 7]. Furthermore, the spatial distribution of rain gauges is often highly uneven in practice, with few gauges in remote areas, in less developed regions, in areas with complicated terrain, or in forested areas [8]. As a result, in situ rain gauge data seldom matches the needs of applications that require precipitation data with high spatiotemporal resolution [8; 9]. This is particularly true across vast swaths of the African continent [10]. As opposed to rain gauges, gridded precipitation products (GPPs) from merged satellite-/gauge-/model-based rainfall products have the advantage of offering wide spatial coverage [1; 11]. There are currently a number of GPPs available, including ARC2 (African Rainfall Estimate Climatology version 2), CHIRPS (Climate Hazards Group Infrared Precipitation with Station Data), ERA5 (European Centre Medium-Range Weather Forecasts Reanalysis), IMERG v06 Final (Integrated Multi-satellite Retrievals for Global Precipitation Measurement), PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System), and RFEv2 (African Rainfall Estimation version 2), among others [1]. Recent GPPs also provide adequate spatial (\(\leq\)0.1 \(\times\) 0.1\({}^{\circ}\)) and temporal (daily to sub-hourly, depending on the product) resolution, allowing for credible precipitation estimates in data-scarce environments or at ungauged locations [1; 12; 13]. They have been used in a variety of applications, including hydrological modeling, extreme event analysis, infrastructure design (based on rainfall frequency analysis), and water resource management [8; 14; 15]. Since GPPs are based on indirect rainfall estimation methods, the results will be subject to uncertainty due to measurement errors, sampling, retrieval methods, and bias correction processes [16; 17]. The errors depend on the number and type of sensors taking measurements across a certain location at a given time, as well as the strategies used to assimilate the available data into a coherent gridded dataset [18; 19]. Furthermore, the error characteristics differ based on the type of storm system, location, topography, and cloud properties [19]. Therefore, the accuracy of GPPs must be thoroughly explored both in time and space [20; 21], and quantitative statistical evaluations are useful tools for assessing GPP precision [20; 22]. Whereas some researchers assess GPPs based on the accuracy of streamflow rate predictions within hydrological modeling frameworks [22; 23], most studies evaluate GPPs against gauge data or against estimates from ground-based weather radars [9; 24]. Various studies have been undertaken to assess GPP performance at the global, continental, and regional levels during the last few decades. TRMM Multi-Satellite Precipitation Analysis (TMPA) products, for example, have been assessed in various parts of Africa, and the results revealed that TMPA products provide effective data in most regions [6; 25]. In [16], the authors found that TMPA was the best product at a daily time scale over different parts of Central Africa. Following that success, ref. [20] proved that the Integrated Multi-satellite Retrievals for GPM (Global Precipitation Measurement), which integrates observations from many satellites of the GPM satellite constellation, improves the quality and spatiotemporal resolution of precipitation data.