\begin{table}
\begin{tabular}{c c c} \hline
**Region** & **POD** & **FAR** \\ \hline SW & 22.58\% & 0.00\% \\ NE & 62.50\% & 11.46\% \\ NW & 0.00\% & 0.00\% \\ YR & 28.57\% & 0.00\% \\ Total & 24.83\% & 3.32\% \\ \hline \end{tabular}
\end{table} 
Table 4: The _POD_ and _FAR_ of the drought characterization approach in four different regions. Figure 5: The comparison of the drought events determined by the local meteorological drought data and the drought characterization approach based on GRACE-DSI during 2006–2015 (the red bars represent the drought severity, orange bars represent the drought area percentage, and the gray bars represent the drought duration determined by the local meteorological drought data). Figure 6 and Table 6 show the corresponding results of the calibrated drought characterization approach. The threshold of monthly GRACE-DSI and drought area percentage of SW and NE are \(-0.1\) and \(10\%\), respectively, while the ones of NW and YR are \(-0.2\) and \(10\%\), respectively. Comparing Tables 4 and 6, we found that after the local drought data calibration processing, total FAR increased by 20.2% and the increase in total \(POD\) was 65.69%. Among them, the \(POD\) of the four study regions (SW, NE, NW, and YR) is 83.87%, 87.50%, 94.29%, and 96.43%, respectively, while the \(FAR\) of the four study regions is 12.07%, 32.29%, 24.71%, and 25.00%, respectively. Although \(FAR\) increased, the increase of \(POD\) is much greater than that of \(FAR\). Overall, the effect of restraint is more obvious. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Region** & \(POD\) & \(FAR\) \\ \hline SW & 83.87\% & 12.07\% \\ NE & 87.50\% & 32.29\% \\ NW & 94.29\% & 24.71\% \\ YR & 96.43\% & 25.00\% \\ Total & 90.52\% & 23.52\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The POD and FAR of the drought event characterization approach in four different regions during 2006 and 2015. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Drought Area Percentage**} & \multicolumn{3}{c}{**Monthly GRACE-DSI**} \\ \hline
**Data Range** & **Frequency** & **Percentage** & **Data Range** & **Frequency** & **Percentage** \\ \hline
0–10\% & 7 & 5.30\% & 0—0.1 & 6 & 4.84\% \\
10\%–20\% & 11 & 8.33\% & \(-0.1\)—0.2 & 7 & 5.65\% \\
20\%–30\% & 8 & 6.06\% & \(-0.2\)—0.3 & 9 & 7.26\% \\
30\%–40\% & 9 & 6.82\% & \(-0.3\)—0.4 & 9 & 7.26\% \\
40\%–50\% & 11 & 8.33\% & \(-0.4\)—0.5 & 11 & 8.87\% \\
50\%–60\% & 10 & 7.58\% & \(-0.5\)—0.6 & 12 & 9.68\% \\
60\%–70\% & 17 & 12.88\% & \(-0.6\)—0.7 & 12 & 9.68\% \\
70\%–80\% & 21 & 15.91\% & \(-0.7\)—0.8 & 15 & 12.10\% \\
80\%–100\% & 38 & 28.79\% & \(-0.8\)—\(\infty\) & 43 & 34.68\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Frequency table of the drought area percentage and monthly GRACE-DSI in four study regions. Figure 6: The drought characteristics determined by the calibrated drought characterization approach during 2006–2015. In order to verify the effect of the calibration effect, we calculated the results of _POD_ and _FAR_ of the calibrated approach from 2016 to 2019. The results are shown in Table 7 and Figure 7. According to the results, we found that after improvement, the total _POD_ and _FAR_ of the drought characterization approach in the four study regions are 98.08% and 19.67%. Among them, the _POD_ and _FAR_ of SW and YR are the same, which are 100.00% and 0.00%, respectively. At the same time, the _POD_ of NE and NW is also very high, at 92.31% and 100.00%, respectively. However, the drought characterization approach has a certain _FAR_ in these two regions, which are 40.91% and 37.78%. Through the above analysis, it can be seen that the improvement measures in this paper have a significant effect on the accuracy of the drought characterization approach.