## 3 Results and Discussion
### Climate Attribution  
The variation in GPP is closely related to climate [20; 31], and the correlation parameters within the studied ESMs are provided in Table 1. It can be seen that GPP is correlated significantly with at least one climate variable. There are cases in which GPP correlates negatively with temperature, such as in the temperate continental zone with MPI-ESM1-2-HR and in the temperate monsoonal zone with BCC-CSM2-MR, which imply inherent differences in ecological modeling between the different ESMs [9; 10]. On the Tibetan Plateau, it is unanimous within the ESMs that GPP is most closely related to temperature rather than to precipitation. Because the ESMs substantially overestimate precipitation over the Tibetan Plateau [32; 33; 34], it is possible that precipitation is not the primary climate factor constraining the regional ecosystem. Figure 4: Seasonal GPP anomaly distribution under 1.5 \({}^{\circ}\)C of global warming (gC m\({}^{-2}\) yr\({}^{-1}\)). The four rows from up to down represent spring, summer, autumn, and winter, respectively. The three columns from left to right represent BCC-CSM2-MR, CMCC-CM2-SR5, and MPI-ESM1-2-HR, respectively. The corresponding climate changes--i.e., precipitation and surface air temperature-- under the warming targets are shown in Tables 2 and 3, respectively. The mode of temperature change in the four regions is consistent among the models. The hottest region--i.e., the subtropical-tropical monsoonal zone--increases least under the effects of warming. Both CMCC-CM2-SR5 and MPI-ESMI-2-HR produced similar estimates of temperature change, whereas the estimates from BCC-CSM2-MR were larger, especially over the monsoonal regions. There is a greater model variety regarding the change in precipitation. Under 1.5 \({}^{\circ}\)C of warming, there are negative changes--e.g., in the temperate monsoonal zone with CMCC-CM2-SR5 and in the subtropical-tropical monsoonal zone with both BCC-CSM2-MR and CMCC-CM2-SR5. Conversely, under 2 \({}^{\circ}\)C of warming, there are no negative changes, but the incremental differences for one certain region are huge. These results indicate the large uncertainty in the precipitation projections made by the ESMs. It is also worth noting  
Figure 5: Seasonal GPP anomaly distribution under 2 \({}^{\circ}\)C of global warming (gC m\({}^{-2}\) yr\({}^{-1}\)). The four rows from up to down represent spring, summer, autumn, and winter, respectively. The three columns from left to right represent BCC-CSM2-MR, CMCC-CM2-SR5, and MPI-ESMI-2-HR, respectively. that in comparison with their variabilities, the change in temperature under the warming scenarios is reasonably large, whereas the precipitation change is rather limited [10]. The apparent sensitivity of climate to the ecosystem of each of the ESMs is shown in Table 4. The response of the ecosystem to climate varies strongly among the models. For some ESM regions, climate plays a crucial role, such that the variation in climate explains more than half of the variation in GPP. However, for certain other ESM regions, the degree of explanation attributable to climate is rather small--e.g., MPI-ESM1-2-HR in the subtropical-tropical monsoonal zone and BCC-CSM2-MR on the Tibetan Plateau. For one particular region, the same climate factor might affect the ecosystem differently in the various models. Taking the subtropical-tropical monsoonal zone as an example, precipitation is the major influencing factor and affects the ecosystem positively with BCC-CSM2-MR and CMCC-CM2-SR5. However, with MPI-ESM1-2-HR, the correlation between precipitation and GPP is insignificant and negative (Tables 1 and 4). Moreover, the overall climate contribution to ecosystem variation with MPI-ESM1-2-HR is very small (Table 4). These features further reflect the inherent differences of ecological modeling within the ESMs.