## 3 Results  
### Spatio-Temporal Variation Characteristics of GPP from Different Data Sources  
In terms of spatial variation characteristics (Figure 4), GPP simulated by ISIMIP for grassland, cropland, and forest in China from 1982 to 2018 indicated that 71.99%, 51.97%, and 98.49% area of grassland, cropland, and forest lands exhibited a rising trend, with the proportions that showed a significant increasing trend being 58.89%, 20.79%, and 87.79%. Similarly, in MSTMIP, 61.74%, 57.58%, and 58.36% of the area of grassland, cropland, and forest lands showed increasing trends of GPP, with the proportions that showed a significant increasing trend being 20.80%, 13.48%, and 11.71%. However, there was a decreasing trend of grassland GPP in Xinjiang and cropland GPP in the North China plains, while MSTMIP GPP showed a decreasing trend in the cropland of Northeast China and forest near the Sichuan basin. The two remote sensing GPP datasets exhibited similar spatial variation trends in grassland, with over 81% of the area displaying an increasing trend from 1982 to 2018, and the proportions of GLASS and MuSyQ grass GPPs that showed a significant upward trend were 53.63% and 50.51%, respectively. However, the trends of cropland and forest GPPs in China differed. In the MuSyQ dataset, a higher percentage of grid points showed an increasing trend, reaching 89% for cropland GPP and 84% for forest GPP, respectively. This phenomenon is likely due to the inclusion of the Cleanness Index (CI) in the MuSyQ dataset. The CI indirectly represents the varying proportions of direct and indirect radiation in incident solar radiation, incorporating the impacts of light, moisture, and temperature conditions on light energy utilization [28]. This leads to an enhanced light use efficiency (LUE) when compared to the GLASS dataset, consequently affecting the dataset's GPP. The absolute variation in GPP values across different sources was relatively large (Figure 5), yet the overall trend was upward, reflecting the inter-annual fluctuations of different vegetation types in China's terrestrial ecosystems. Grassland GPP growth rates in China ranged from 0.23 to 3.16 g C m\({}^{-2}\) year\({}^{-1}\) and cropland GPP growth rates in China ranged from 0.40 to 7.32 g C m\({}^{-2}\) year\({}^{-1}\); the forest GPP in China exhibited the most pronounced increasing trend, with an average growth rate ranging from 0.67 to 7.81 g C m\({}^{-2}\) year\({}^{-1}\) (Figure 5d). We observed a higher increasing rate in GPP in cropland compared to grassland. The increase in cropland GPP is closely linked to human activities. In recent years, modern farming practices like precision irrigation, optimal fertilization, and the cultivation of high-yield crop varieties have significantly enhanced photosynthetic efficiency and GPP [31]. Furthermore, climate change, including warmer temperature, longer carbon uptake period, and higher CO\({}_{2}\) concentration, has also occurred. Cropland is more sensitive to temperature and CO\({}_{2}\) levels [32], resulting in a higher GPP increase compared to grassland. In summary, both model-based and remote sensing-based spatial and temporal analyses confirmed a significant increase in GPP across China's terrestrial ecosystems over the last four decades, and these ecosystems have become more efficient carbon sinks compared to the previous century. Figure 4: Spatial patterns of GPP changes of terrestrial ecosystems in China from 1982 to 2018 based on models and remote sensing observations. (**a,b**) are trends on grid-wise GPP 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 trends on grid-wise GPP changes averaged by GLASS and MuSyQ datasets, respectively. The regional average trend values are counted as box plots in the lower left corner.