## 1 Introduction  
Flooding is among the most prevalent natural disasters around the world, with particularly heavy socio-economic impacts in low-income countries [1]. In recent decades, the frequency and intensity of floods have been increasing, which can cause critical damage to property and loss of life, especially in densely populated low-lying coastal areas [2, 3, 4]. As precipitation is a key component of the global water cycle and the primary input for hydrological models, its accurate measurement is vital to improving our understanding of meteorology, hydrology, and agriculture [5, 6]. Although ground-based rain gauges, weather radar networks, and ocean-based instruments can observe more accurate precipitation at points or on small scales, they are usually sparsely and unequally spatially distributed [7, 8]. Moreover, hydrological modeling and forecasting is a challenging task in ungauged and data-scare basins, especially in remote areas with complex terrains in developing countries [9; 10; 11]. This indicates that the restricted in-situ observations and weaknesses leave a knowledge gap in our understanding of complex hydrological processes and water resource management [12]. Following rapid advances in remote sensing technologies, satellite sensors and reanalysis models can provide continuous, open-access, and quasi-global precipitation estimates at high spatial and temporal resolutions [13; 14; 15]; examples include the Precipitation Estimates from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) [16], the gauge-corrected version of Global Satellite Mapping of Precipitation (GSMaP) [17], the Tropic Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) [18], and the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM IMERG) [19]. In addition, the latest fifth generation of the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis database (ERA5) [20] integrates a large amount of historical observations into global estimates using advanced modelling and data assimilation systems. Although these gridded remote-sensing-based precipitation products have been broadly applied in numerous fields at different temporal and spatial scales, their performances are greatly restricted by errors and show areas for further improvement [21; 22; 23; 24; 25; 26; 27]. On the one hand, the quality of satellite precipitation data varies with climate, topography, temporal variability, and precipitation intensity, which may be limited by the satellite sensors, retrieval algorithms, and spatial sampling frequency [28]. On the other hand, uncertainties in reanalysis precipitation datasets can arise from the forcing data and the prediction model in the assimilation system, making their performances inconsistent in different regions [29]. Consequently, it is essential to assess the reliability and applicability of multi-source precipitation products and to make some adjustments and improvements for better hydrometeorological applications. In recent years, many studies have investigated the hydrological utility of commonly used satellite and reanalysis precipitation products for streamflow simulation during different time periods at basin or global scales [30; 31; 32; 33; 34; 35]. Several other studies have evaluated the error propagation of satellite precipitation uncertainties through hydrological modeling over different areas [36; 37; 38; 39]. Nevertheless, previous studies have mainly evaluated multi-satellite and reanalysis precipitation products combined with statistical indicators using lumped hydrological models, thereby excluding the internal spatially variable characteristics of river basins [32; 40; 41; 42; 43]. Often, hydrological models have not been re-calibrated for each remotely sensed precipitation product; thus, they fail to compare uncorrected satellite or reanalysis products with the ones based on rain gauge networks [43].