# Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning
## 5 Conclusions  
In this study, a GPP-merged dataset with high resolution in China has been developed using the deep learning method. It combines the advantages of 23 CMIP6 GPP datasets without relying on any prior knowledge. The lower resolution of the multi-model data has been improved to 0.25\({}^{\circ}\) of the merged GPP. From the perspective of evaluation indicators, the performance of merged data is superior to the others in all aspects. In China, the error has been kept at a low level. The merged dataset has significantly improved in temporal and spatial dimensions compared with every single model and the multi-model mean. The merged data are closer to the actual value than the multi-model mean according to the fluxnet data. The merged data greatly reduce the uncertainties and improve the reliability of GPP estimation in the projection period. A further analysis of the spatiotemporal change pattern of GPP in China has resulted in the following findings. During the baseline, GPP is the highest in summer, followed by spring and autumn, and the lowest in winter due to the combined effects of factors such as the monsoon climate and vegetation types. GPP exhibits significant spatial heterogeneity due to differences in topography, vegetation types, and hydrothermal conditions in different regions. Generally, GPP increases gradually from the northwest inland to the southeast coast. GPP in the southern part of Yunnan remains high in winter. According to the long-term variation trends, GPP is increasing in most regions of China. It is worth noting that the characteristics of the decrease in the southern coastal areas and Inner Mongolia are not captured by the multi-model ensemble mean; however, they are captured by the merged GPP. There are seasonal differences in the variations in GPP relative to the baseline under different scenarios in the future projection period, while the variations are almost the same across different scenarios in the near future. In summer, GPP increased rapidly with the increase in radiation forcing levels. In winter, GPP decreases in a large region mainly distributed in the west, Inner Mongolia, and Heilongjiang. The long-term variation trends of GPP in China under different scenarios are significantly different. The growth rate of GPP has reached the maximum under the SSP5-8.5 scenario since 2036. The increasing trend becomes slightly slower under the SSP3-7.0 scenario than under the high-emission scenario. Furthermore, GPP decreased significantly under the SSP1-2.6 scenario. The annual GPP in each pixel in China shows an increasing trend, which is more intense with the increase in radiation forcing levels. GPP changes from increasing to decreasing over time, since GPP is affected by temperature and carbon dioxide under the SSP1-2.6 scenario. Under the SSP2-4.5 scenario, GPP will increase in the near future; however, the trend will slow down as time goes by. Under the SSP3-7.0 and SSP5-8.5 scenarios, GPP increases with time; the increasing trend rises gradually. The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/f14061201/s1](https://www.mdpi.com/article/10.3390/f14061201/s1)]([https://www.mdpi.com/article/10.3390/f14061201/s1](https://www.mdpi.com/article/10.3390/f14061201/s1)), Figure S1: The probability density distribution of GLASS GPP during 1982-2014, Figure S2: Spatial distribution of the multi-annual mean GPP in far future (2071-2100) relative to baseline under (a1-a4) SSP 1-2.6, (b1-b4) SSP2-4.5, (c1-c4) SSP 3-7.0, (d1-d4), and SSP 5-8.5 in (a1-d1) spring, (a2-d2) summer, (a3-d3) autumn, and (a4-d4) winter, Figure S3: Spatial distribution of the variation trends of annual average GPP under four climate scenarios in the near, middle, and far future, with the dot indicating passing significance test at 5% level. Conceptualization, J.L. and G.W. ; methodology, J.L. and D.F. ; software, J.L. and D.F. ; validation, D.F. and I.K.N. ; formal analysis, J.L. ; investigation, J.L. ; resources, D.F. ; data curation, I.K.N.