## 3 Results
### Distribution of the Sensitivity of NDVI-GPP  
The sensitivity of GPP to NDVI, expressed as the slope of the liner regression between NDVI and GPP, exhibited an inconsistent pattern. As shown in Figure 6a, slope values ranged from \(-0.90\) to \(1.21\), with approximately 71.37% of the area showing a positive slope. This indicated that NDVI has a positive effect on GPP. Particularly in the northwest region, higher slope values were exhibited, with an average value of \(0.11\). Conversely, in the southwest of China, the sensitivity of GPP to NDVI was lower, with the slope values below 0. These results are consistent with previous studies; that is, there is a strong linear relationship between NDVI and GPP, but there were spatial variations in the distribution of R\({}^{2}\) among different regions. The R\({}^{2}\) between NDVI-GPP was higher in northern China, while the sensitivity of NDVI to GPP was lower in northwest China. This suggested that the relationships between NDVI and GPP, in terms of R\({}^{2}\) and sensitivity, are spatially asynchronous. Meanwhile, we analyzed the density of the slope values of the NDVI-GPP relationship for all pixels (Figure 6b). The analysis revealed that the frequency distribution of the slope values closely resembles a Gaussian distribution. Most of the slope values were clustered around a central value, forming a bell-shaped curve typical of Gaussian distributions. The mean value of the slope distribution was calculated to be 0.11, suggesting that, on average, there is a moderately positive correlation between NDVI and GPP across China. This indicates that as vegetation greenness (NDVI) increases, there is a corresponding increase in productivity (GPP), reflecting a general trend where more vigorous vegetation supports higher levels of primary productivity across diverse ecosystems. To explore the sensitivity of GPP to NDVI among aridity gradients, we calculated the mean values for each pixel over the period from 1982 to 2018 and then fitted the slope of NDVI and GPP (Figure 7). The analysis revealed that GPP was more sensitive to NDVI in hyper-arid areas, with a slope of 0.12. As the AI gradient increased (indicating more humid conditions), the slope showed a decreasing trend: 0.10 in arid regions, 0.09 in semi-arid regions, 0.06 in sub-humid regions, 0.04 in humid regions, and 0.01 in hyper-humid regions. This pattern highlighted the critical impact of moisture availability on the sensitivity of  
Figure 6: Spatial distribution of the (**a**) sensitivity between GPP and NDVI in the growing season during the period of 1982–2018 and (**b**) their density distribution. vegetation productivity to changes in NDVI. The decreasing slope values along with the humidity gradient underline the varying degrees of responsiveness of different ecosystems to environmental changes. To address the driving factors behind the temporal variability of vegetation response to drying and wetting, we performed a sensitivity analysis to attribute the changes in PFTs (Figure 8). Positive slope values were found between NDVI-GPP in all ecosystems except for ENF. In ENF, the sensitivity value was \(-0.01\), indicating that the increase in NDVI did not lead to an increase in GPP. The largest sensitivity values were observed in CRA, with a sensitivity value of 0.07, suggesting a strong positive response of GPP to NDVI in this ecosystem. In contrast, CRO showed lower sensitivity values of 0.04. These findings highlighted the varying degrees of vegetation response to drying and wetting conditions across different PFTs. The high sensitivity of GPP to NDVI in GRA emphasizes that changes in NDVI in grassland ecosystems can have a greater impact on GPP. Figure 8: The sensitivity of NDVI-GPP during the growing season from the period of 1982–2018 across all PFTs. The asterisk (*) indicates a significant increase or decrease in slope. Figure 7: The sensitivity of NDVI-GPP during the growing season period of 1982–2018 across all AI gradients. The asterisk (*) indicates a significant increase or decrease in slope.