## 3 Results
### The Relationship between Meteorological Drought Indices and Vegetation GPP  
To characterize the influence of water deficit on vegetation, the Pearson correlation was applied to evaluate the correlations between GPP and meteorological drought indices. As shown in Figure 5, 70.7% of the total area showed that SPEI and GPP were positively correlated. In addition, 9.8% of the area showed a significant positive correlation (\(p<0.05\)), and 6.1% showed a highly significant positive correlation (\(p<0.01\)). The regions that showed a significant positive correlation were mainly concentrated in northern China. For different seasons, the proportion of positively correlated GPP and SPEI is larger in summer and autumn, accounting for 64.6% (with 27.9% having a significant positive correlation, namely, \(p<0.05\) and \(p<0.01\)) and 65.3% (with 11.1% having a significant positive correlation, namely, \(p<0.05\) and \(p<0.01\)), respectively. On the contrary, the proportion of positive correlation in spring and winter is smaller, accounting for 37.6% and 43.2%, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vegetation Types** & **SPEI** & **VPD** & **GPP** \\ \hline Evergreen Forests & 46.4 & 49.7 & 80.1 \\ Dechtous Forests & 16.2 & 27.6 & 97.5 \\ Mixed Forests & 15.1 & 25.2 & 90.4 \\ Woody Savannas & 27.1 & 31.2 & 96.8 \\ Savannas & 20.7 & 37.1 & 96.1 \\ Grasslands & 32.6 & 65.1 & 87.2 \\ Croplands & 41.6 & 54.7 & 93.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The area proportions of the annual decreasing trend of SPEI, increasing trend of VPD, and increasing trend of GPP from 2001 to 2020 across China, under different vegetation types (unit: %). Figure 6 shows the frequency distribution (%) of the areas with a positive correlation (\(R>0\)) between GPP and SPEI among different seasons and vegetation types. At the annual scale, water deficit had the greatest negative impact (\(R>0\)) on GPP in savannas (80.0%) and the least impact on evergreen forests (54.8%). The sensitivity of vegetation to water deficit varied in different seasons. Except for grasslands, all vegetation types showed the strongest sensitivity in autumn. In spring, savannas were the most sensitive to water deficit. Grasslands were the most sensitive to water deficit in both summer and winter, while in autumn, croplands were the most sensitive to water deficit. We further examined the correlation between GPP and VPD. As shown in Figure 7, 66.2% of the total area showed that GPP and VPD were negatively correlated, indicating that water deficit has a particular inhibitory effect on GPP. In addition, 9.9% of the area showed a significant negative correlation (\(p<0.05\)), and 11.6% showed a highly significant negative correlation (\(p<0.01\)). Similar to SPEI, the regions that showed significant negative correlations were mainly concentrated in northern China. For different seasons, the proportion of negatively correlated GPP and VPD was larger in summer and autumn, accounting for 64.9% (with 28.8% having a significant positive correlation, namely, \(p<0.05\) and \(p<0.01\)) and 64.2% (with 13.6% having a significant positive correlation, namely, \(p<0.05\) and \(p<0.01\)), respectively. On the contrary, the proportion of negative correlation in spring and winter was smaller, 34.0% and 43.7%, respectively. Figure 3: Spatial distribution of annual (**a**) changing trend, (**b**) \(p\)-value, and seasonal changing trend in (**c**) spring, (**d**) summer, (**e**) autumn, and (**f**) winter of vapor pressure deficit (VPD) across China from 2001 to 2020 based on the Mann–Kendall (M–K) trend test. Figure 4: Spatial distribution of annual (**a**) changing trend, (**b**) \(p\)-value, and seasonal changing trend in (**c**) spring, (**d**) summer, (**e**) autumn, and (**f**) winter of gross primary productivity (GPP) across China from 2001 to 2020 based on the Mann–Kendall (M–K) trend test.