# Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning
## 4 Discussion  
It can be seen from Figure 10 and Figure S2 that the variations in GPP in the north and south of China are opposite in winter while increasing in all regions in spring, summer, and autumn. However, this cannot reflect the impact of the reduction in winter on the change in the average GPP across the country. Therefore, Figure 12 shows the inter-annual changes in GPP in China under the historical baseline and different scenarios in the future. The thick grey line represents the line between the baseline and the future. GPP shows an increasing trend from 1.76 gCm\({}^{-2}\)d\({}^{-1}\) to 1.84 gCm\({}^{-2}\)d\({}^{-1}\) from 1995 to 2014. It may result from a combination of factors such as appropriate climate change, the fertilization effect of CO2, nitrogen deposition, and human activities such as afforestation and agricultural  
Figure 11: Spatial distribution of the variation trends of annual average CMIP6\({}_{\text{DL}}\) GPP under **(a)** SSP1-2.6, **(b)** SSP2-4.5, **(c)** SSP3-7.0, and **(d)** SSP5-8.5 from 2015 to 2100. Shaded values have passed the significance test at the 5% level. irrigation [68; 69; 70; 71]. From 2015 to 2035, GPP increases steadily yearly with similar increasing trends under the four scenarios. From 2036, the difference in the variations in GPP in the four scenarios is noticeable. Among them, the variation speed of GPP is the fastest under SSP5-8.5, with the average reaching 2.49 g\(\mathrm{cm}^{-2}\mathrm{d}^{-1}\) at the end of the 21 st century. The second fastest occurs under SSP3-7.0, which is slightly slower than that of the high-emission scenario. The annual average is roughly the same before 2055 as under SSP2-4.5. However, the gap between the two scenarios gradually increases from 2067, when the growth rate under SSP2-4.5 slows down. From 2081 to 2100, it is almost stable and begins declining slowly in 2097. Among the four scenarios, GPP decreases significantly only in the SSP1-2.6 scenario. Although there is a decreasing trend at the end of the century under the SSP2-4.5 scenario (Figure 12), GPP shows an overall increasing trend in each pixel over the period 2015-2100 (Figure 11b). More specifically, it keeps increasing, as do the other three scenarios in the near future, followed by a slight fluctuation around 1.96 g\(\mathrm{cm}^{-2}\mathrm{d}^{-1}\) for a long time in the medium term. It is worth noting that GPP declines significantly in the far future, especially from 2075 onwards. Meteorological factors are the dominant factors in the interannual variation in global GPP. In the context of climate warming, the frequent occurrence of extreme events such as high temperatures and droughs is expected to impact vegetation growth, leading to fluctuations in ecosystem GPP. According to the definition of severe climate, the Intergovernmental Panel on Climate Change [72] pointed out that climate change has led to changes in the frequency, intensity, spatial scope, duration, and time of extreme climate, which may have an unprecedented impact on the terrestrial carbon cycle. In addition, due to global warming, climate change is expected to further increase the frequency, persistence, and intensity of extreme weather in the mid to late 21 st century [73; 74; 75], making the impact of future climate change on terrestrial ecosystems even more uncertain [76; 77]. Large-scale studies using merged GPP can reduce the uncertainty of terrestrial ecosystem carbon cycle research. However, the merging process will also introduce certain uncertainties, mainly affected by input errors, scale effects, and merging algorithms. Input errors are caused by the CMIP6 datasets, GLASS GPP, and flux tower observation GPP. Although model simulation provides an essential research tool for the carbon cycle of large-scale terrestrial ecosystems, there are still significant differences in the simulation results of the models at regional and global scales due to the differences in the structure, parameter settings, input data, and spatial resolution of each CMIP6 model. [PERSON] et al.