## Materials and Methods
### Methodology
#### 2.3.2 Evaluation Indicators  
Table 2 shows the statistical and hydrological metrics used for comparison and evaluation in this study. Five statistical indicators--i.e., the Pearson correlation coefficient (CC), relative bias (BIAS), mean absolute error (MAE), root-mean-square error (RMSE), and Kling-Gupta efficiency (KGE)--were selected to quantitatively assess the agreement of multi-satellite and reanalysis precipitation estimates against gauge-observed data. Another three detection indicators--i.e., the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI)--were used to evaluate the detection ability above the daily rainfall threshold of 1.0 mm/d [66; 30].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Full Name of the Dataset** & **Resolution** & **Period** & **Reference** \\ \hline Satellite precipitation dataset & & & & \\ IMERG-cal & \begin{tabular}{c} Integrated Multi-satellite Retrievals \\ for GPM Final run V06B \\ Integrated Multi-satellite Retrievals \\ for GPM Final run \\ TRMM (TMPA) Multi-satellite \\ Precipitation Analysis 3B42V7 \\ TRMM (TMPA) Near Real-Time \\ Precipitation 3B42 RT \\ Precipitation Estimation from \\ RESTIANN-CCS \\ \end{tabular} & 0.1\({}^{\circ}\)/0.5 h & 2006–present & [19] \\  & \begin{tabular}{c} Integrated Multi-satellite Retrievals \\ for GPM Final run \\ TRMM (TMPA) Multi-satellite \\ Precipitation Analysis 3B42V7 \\ TRMM (TMPA) Near Real-Time \\ Precipitation 3B42 RT \\ Precipitation Estimation from \\ Remotely Sensed Information using \\ Artificial Neural Networks-Cloud \\ Classification System \\ GSMaP \\ Global Satellite Mapping of Precipitation V6/V7 \\ GSMaP-RNL \\ \end{tabular} & 0.1\({}^{\circ}\)/1 h & 2000–present & [55] \\  & \begin{tabular}{c} Preipitation Project \\ \end{tabular} & 0.1\({}^{\circ}\)/1 h & 2000–present & [17] \\  & \
\begin{tabular}{c} Fifth generation of ECMWF \\ atmospheric reanalyses of the \\ global climate \\ ECMWF ReAnalysis Interim \\ Merged precipitation product \\ \end{tabular} & 0.25\({}^{\circ}\)/1 h & 1979–present & [56] \\  &
\begin{tabular}{c} The arithmetic mean of 3B42V7, \\ IMERG-cal, and GSMaP \\ The arithmetic mean of 3B42 RT, \\ IMERG-uncal, and ERA5 \\ \end{tabular} & 0.083\({}^{\circ}\)/1 d & 2006–2018 & \\ \hline \end{tabular}
\end{table}
Table 1: Description of multiple satellite- and reanalysis-based precipitation products utilized in this research.  
According to the standards of the Precipitation Intensity Classification adopted by the China Meteorological Administration ([[https://www.cma.gov.cn/](https://www.cma.gov.cn/)]([https://www.cma.gov.cn/](https://www.cma.gov.cn/)), accessed on 16 August 2013), daily rainfall was divided into five categories: no/tiny rain (<0.1 mm/d), light rain (0.1-10 mm/d), moderate rain (10-25 mm/d), heavy rain (25-50 mm/d), and storm (\(\geq\)50 mm/d), respectively.  
In addition, a Taylor diagram was utilized to directly analyze the error characteristics of rainfall datasets in the calibration and validation periods; it comprises the CC, standard deviation (STD), and centered root-mean-square deviation (RMSD).  
For hydrologic model performance, the Nash-Sutcliffe coefficient of efficiency (NSCE), CC, BIAS, and RMSE were generally used to evaluate the streamflow simulations. Then, the Quantile-Quantile (Q-Q) plot was selected to assess whether two datasets were generated from the same distribution.