## 3 Results
### Spatial and Temporal Trends in GPP\({}_{\text{max}}\) from Different Data Sources  
Plant photosynthesis is crucial for understanding plant physiology and its response to environmental changes. As the maximum photosynthetic carbon uptake represents the important characteristics of plant photosynthesis, it can be used as an indicator of plant physiology. The monthly-scale GPP data for China's terrestrial ecosystems was analyzed to extract the maximum value for each year, termed GPP\({}_{\text{max}}\), to map the spatial pattern of long-term trends. The spatial patterns of the GPP\({}_{\text{max}}\) trends derived from both model-based simulations and remote sensing closely mirrored those of the overall GPP data. All trends exhibited significant increases over time.  
Discrepancies in GPP\({}_{\text{max}}\) were observed between model simulations and remote sensing data from different sources (Figure 6). In the ISMIP dataset, GPP\({}_{\text{max}}\) exhibited rapid growth, averaging 1 g C m\({}^{-2}\) year\({}^{-1}\) across most regions except the Xinjiang and the Loess Plateau grassland, with a particularly notable increase in the northeastern cropland, whereas the MsTMP GPP\({}_{\text{max}}\) simulated by the same model exceeded 1 g C m\({}^{-2}\) year\({}^{-1}\) solely in the grassland areas of the southern Tibetan Plateau and the cropland of North China plain, with a less pronounced upward trend in the Northeast cropland. The spatial trend patterns of GPP\({}_{\text{max}}\) in Chinese grassland derived from two remote sensing-based models were largely consistent. Both models indicated a decline in GPP\({}_{\text{max}}\) within the grassland of eastern Inner Mongolia. Moreover, the forest of southeastern China exhibited an increasing trend in GPP\({}_{\text{max}}\), which was not captured by the GLASS dataset. This showed the inadequacy of relying solely on one data source for a comprehensive analysis of GPP and GPP\({}_{\text{max}}\) trends. Multiple GPP datasets are essential for comparison to achieve a detailed understanding of the spatial and temporal dynamics of the ecosystem GPP in China.  
Figure 6: Spatial patterns of GPPmax changes in China’s terrestrial ecosystems over the past four decades based on model and remote sensing observations. (**a,b**) are the direction and magnitude ofGPPmax changes averaged by the multi-model combinations of ISMIP and MsTMIP, respectively. The dots in the graph represent that this point passes the significance test of \(p<0.05\). (**c,d**) are the direction and magnitude of GPPmax changes driven by GLASS and MuSyQ datasets, respectively. The regional average trend values are counted as box plots in the lower left corner. Data points that are more than 1.5 times the interquartile range of the first and third quartiles are marked as outliers, which are indicated by red dots in the figure.  
When comparing the interannual changes in GPPmax of China's terrestrial ecosystems, differences emerged between the model and remote sensing GPPmax. Specifically, the GPPmax values from MsTMIP for cropland and forest were lower than those from the other three data sources, but overall, there was a more consistent fluctuating increase (Figure 7). Grassland GPPmax ranges from 60 to 120 g C m\({}^{-2}\), while cropland GPPmax typically ranges between 180 and 240 g C m\({}^{-2}\), with the exception of the MsTMIP dataset. Forest remote sensing GPPmax remained relatively constant at approximately 250 g C m\({}^{-2}\). Most grassland areas and cropland as well as forest, except for individual data from a few regions, exhibited GPPmax growth rates exceeding 0.15 g C m\({}^{-2}\) year\({}^{-1}\), 0.09 g C m\({}^{-2}\) year\({}^{-1}\), and 0.18 g C m\({}^{-2}\) year\({}^{-1}\), respectively.