## 5 Conclusions  
This study used the [PERSON] and Sen's methods to analyze the trends of GPP dynamics in China from 1982 to 2015 and explore the impacting factors behind these variations. Our aim was to contribute to understanding the response of vegetation growth to climatic variation and human activities at a national scale. On this foundation, we further used the ARIMA model to forecast the one-year lead GPP values in China and assess the potential and uncertainty of the model. To corroborate the reliability of our results, we also  
Figure 12: Accuracy of predicted GPP using ARIMA obtained by the FluxCom product, the GPPNIRv product, and the GLASS product from 1982 to 2015 and the GOSIF product from 2001 to 2015.  
extended our trend and prediction analyses into multiple sets of GPP products. Our main findings were that:  
(1) Over the whole of China, an upward GPP trend was observed with an average trend of 2.268 g C/m\({}^{2}\)/year during 1982-2015. However, the temporal pattern of GPP trends showed certain heterogeneity, and a more obvious downward trend occurs in the summer. Spatially, the GPP trends in most regions were mainly increasing, but in the SEB and the PRB the trends calculated by five sets of products were not consistent;  
(2) Climate factors and human interventions affect long-term GPP jointly. On a national scale, air temperature is a major climatic factor impacting GPP and the rise in LAI is a major human factor. The effect of precipitation on GPP is mainly reflected in the arid areas in the north and the effect of CO\({}_{2}\) on GPP is reflected in areas with better hydrothermal conditions in the east. AOD and SRAM also affect GPP, but not as obviously as the above factors;  
(3) The ARIMA model performs well in most parts of China, as evidenced by the relatively low values of MAE, RMSE, rMAE and rRMSE, as well as the good consistency with the reference GPP from 2016. Moreover, the performance of the ARIMA model was affected by the values and quality of the GPP data, indicating that the low value and high quality will produce more accurate predictions;  
(4) Further multiple GPP analysis showed consistent seasonality and trends among four sets of products that contained the data from 1982 to 2015. However, there were certain differences in spatial distribution, especially in the CB, the SEB, and the PRB. Using the ARIMA model, five GPP products all achieved satisfactory prediction results which were consistent in space. The comparison of multi-source products showed certain discrepancies in GPP trends and predictions at the basin scale but, on the whole, the conclusions were consistent.  
Conceptualization, Y.B. and K.L.; methodology, Y.B., S.W. and X.L.; formal analysis, X.L. and X.Z.; data curation, K.L., X.Z. and H.Z.; writing--original draft preparation, Y.B.; writing--review and editing, Y.B., K.L., X.L. and X.G.; visualization, X.Z. and H.Z.; supervision, X.L., S.W. and X.G. All authors have read and agreed to the published version of the manuscript.  
This research grants from the National Natural Science Foundation of China (grant no. 42141007) and the Inner Mongolia Autonomous Region Science and Technology Achievement Transformation Special Fund Project (grant no. 2021 CG0045).  
The data presented in this study are available on request from the corresponding author.  
We acknowledge the providers of all the data used in the study.  
We declare that they have no conflict of interest to this work. Additionally, we have no financial and personal relationships with other people or organizations that can inappropriately influence our work.