### Characterization of Drought Events  
We used the calibrated drought characterization approach to detect drought events that occurred in the four study regions from April 2002 to July 2020, which are shown in Figure 8 and Table 8.  
From Figure 8 and Table 8, it can be seen that the number of drought events in the four study regions (SW, NE, NW, and YR) from 2002 to 2020 are 8, 8, 9, and 10, respectively. During this period of time, the time of severe drought events in SW are from September 2002 to June 2004 and from June 2005 to May 2007, the ones in NE are from July 2007 to February 2009 and January 2017 to August 2018, the ones in NW are from July 2002 to July 2003 and from March 2016 to June 2018, and the ones in YR are from September 2002 to July 2004 and from July 2013 to April 2014.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Region** & **POD** & **FAR** \\ \hline SW & 100.00\% & 0.00\% \\ NE & 92.31\% & 40.91\% \\ NW & 100.00\% & 37.78\% \\ YR & 100.00\% & 0.00\% \\ Total & 98.08\% & 19.67\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The _POD_ and _FAR_ of the calibrated approach in four different regions during 2016 and 2019.  
Figure 7: The comparison of the drought events determined by the calibrated drought characterization approach during 2016–2019.  
In order to verify the drought severity determined by this approach, we used the PAI severity derived from local precipitation data to compare the drought severity derived from GRACE-DSI. Due to the many drought events, it is inconvenient to show them one by one. Only the results of the two most severe drought events in each region are shown in Figure 9. From Figure 9, we can see that the spatial distribution of the GRACE-DSI and _PAI_ severity  
Figure 8: Drought events (drought duration, drought severity, and drought-affected area) determined by the calibrated approach in Mainland China during April 2002 and July 2020.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multirow{2}{*}{\begin{tabular}{c} **No. of** \\ **Drought** \\ **Events** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Time Span of** \\ **Drought Events** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Duration** \\ **(months)** \\ \end{tabular} } & \multicolumn{3}{c}{**GRACE-DSI**} & \multicolumn{3}{c}{**Drought Area Percentage**} \\ \cline{6-10}  & & & **Peak** & **Average** & **Peak** & **Average** & **Peak** & **Average** & **Cumulative** \\  & & & **Magnitude** & **Magnitude** & **Severity** & **Magnitude** & **Magnitude** & **Magnitude** & **Manitude** \\ \hline \multirow{10}{*}{SW} & \multirow{8}{*}{8} & 200209–200406 & 22 & \(-1.21\) & \(-0.76\) & \(-16.  
in the regions NE and NW have good spatial similarity, while the similarity between the two data is relatively weak in the regions SW and YR. It may be related to the main factors affecting the occurrence of drought events in SW and YR, not only precipitation but also runoff [32]. Additionally, there are also differences in the spatial distribution of the two data in the four study regions. It is related to the different hydrological components reflected by the two data. GRACE _TWSC_ data represent the total change of various water inputs and outputs that cause _TWSC_. However, precipitation is only one of the water inputs that cause _TWSC_.