## 3 Results
### The Impact of Drought on Vegetation GPP  
The spatial distribution of the correlation coefficients between GPP\({}_{drought\_month}\) and SPEI\({}_{drought\_month}\)/VPD\({}_{drought\_month}\) and the \(p\)-value are shown in Figure 9. We found that the GPP\({}_{drought\_month}\) was negatively correlated with SPEI\({}_{drought\_month}\) over 66.3% of the study area (Figure 9a). Among these, 5.1% showed a significant negative correlation (\(p<0.05\)), which indicated an inhibitory effect of drought on GPP. The area in which GPP\({}_{drought\_month}\) was negatively correlated with VPD\({}_{drought\_month}\) was 48.7%, with only 1.1% showing a significant negative correlation (\(p<0.05\)). Relative to VPD, the drought indicated by SPEI had a more severe negative impact on vegetation GPP. Furthermore, the spatial distribution showed that the correlation coefficients between the two drought indices and GPP are roughly the same during drought periods, and the regions with larger differences are mainly concentrated in northeast and northwestern China. Both drought indices showed that vegetation growth was inhibited during the drought period in northern and southwestern China, as well as in the Qinghai-Tibet Plateau (situated in southwestern China).  
The correlation between GPP\({}_{drought\_month}\) and SPEI\({}_{drought\_month}\)/VPD\({}_{drought\_month}\) for the seven vegetation types in the study area is shown in Figure 10. The correlation coefficients between GPP\({}_{drought\_month}\) and SPEI\({}_{drought\_month}\) were mostly negative. Among them, deciduous forests were the most negatively affected by drought, and woody savannas were the least affected. However, the relationship between GPP\({}_{drought\_month}\) and VPD\({}_{drought\_month}\) of different types of vegetation showed that the proportion of positive correlation was less than that of SPEI\({}_{drought\_month}\). Croplands were the most negatively affected by drought, while woody savannas were the least.  
Figure 8: Frequency distribution of the areas with negative correlation (\(R<0\)) between gross primary productivity (GPP) and vapor pressure deficit (VPD) for different vegetation types (a) evergreen forests, (b) deciduous forests, (c) mixed forests, (d) woody savannas, (e) savannas, (f) grasslands, and (g) croplands under different temporal scales (annual and seasonal) across China from 2001 to 2020.  
Figure 10: Frequency distribution of the areas with correlation coefficients (\(R\)) between \(\text{GPP}_{drought\_month}\) (the average GPP of all drought months in a year) and \(\text{SPEI}_{drought\_month}\) (the sum of the absolute values of SPEI for all drought months in a year; upper panel)/\(\text{VPD}_{drought\_month}\) (the sum of the values of VPD for all drought months in a year; lower panel) for different vegetation types.  
Figure 9: Spatial distribution of (**a**) the correlation coefficients (\(R\)) and (**b**) statistical significance (\(p\)-value) between \(\text{GPP}_{drought\_month}\) (the average GPP of all drought months in a year) and \(\text{SPEI}_{drought\_month}\) (the sum of the absolute values of SPEI for all drought months in a year), and the spatial distribution of (**c**) the correlation coefficients (\(R\)) and (**d**) statistical significance (\(p\)-value) between \(\text{GPP}_{drought\_month}\) and \(\text{VPD}_{drought\_month}\) (the sum of the values of VPD for all drought months in a year).