# Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning
## Results
### Temporal and Spatial Distribution of Merged GPP in Historical Period  
The GPP data over the historical period (1850-2014) and the four climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) are merged using the trained deep learning merging model based on the data simulated by 23 CMIP6 models. The spatial distribution of GPP in different seasons is shown in Figure 8 to study the seasonal variation characteristics during the historical period (1850-2014). There is a significant spatial distribution gradient of GPP in various seasons in China, mainly due to land use and cover conditions and temperature and water environmental factors. In southern China, the climate is warm and humid, with the best hydrothermal conditions, sufficient sunlight and high light utilization efficiency, high vegetation coverage, and the highest GPP in the growing season. GPP is high in northeast forest regions in summer. However, photosynthesis is inhibited by low temperature in other seasons, resulting in relatively low GPP. In the deserts of the northwest, most areas of the Qinghai-Tibet Plateau, and the grasslands in central and western Inner Mongolia, the plant growing season is relatively short due to water stress and low-temperature constraints, which leads to low productivity and even the lowest GPP. Overall, GPP decreases from southeast to northwest and coastal to inland, consistent with current research results [56; 57; 58; 59]. The spatial distribution of GPP is highly compatible with precipitation in summer, indicating that precipitation is an important factor affecting the distribution of GPP in China. In addition, the GPP near the Tianshan Mountains in Xinjiang is relatively high in summer. The prevailing westerly winds from the Atlantic Ocean and the airflow from the Arctic Ocean encounter the uplift of mountain slopes and generate intense precipitation, which promotes a high vegetation coverage in the west and northwest of the Tianshan Mountains [60]. At the same time, the high mountain snowmelt in the Tianshan Mountains also plays a promoting role. The spatial distribution of GPP during the four seasons is significantly different, mainly due to the comprehensive influences of the monsoon climate and vegetation type. Throughout the whole year, GPP is the highest in summer (June to August), followed by spring (March to May), autumn (September to November), and the lowest in winter, which entirely fits within the ecological definition of the GPP itself. In spring, the high value of GPP is concentrated in the area south of 28\({}^{\circ}\) N. As the summer monsoon moves northward, the temperature in most parts increases, with the average GPP in the historical period reaching 13.6 gCm\({}^{-2}\)d\({}^{-1}\). The spatial distribution of GPP is highly affected by hydrothermal conditions. Thus, the high value is mainly distributed in Yunnan and  
Figure 7: Time series of GPP of test dataset in (**a**) arid region, (**b**) semi-arid and semi-humid region, (**c**) Qinghai–Tibet Plateau, and (**d**) humid region. Guizhou. In autumn, the spatial distribution is similar to that in spring due to the decrease in temperature in northern China. GPP is mostly lower than 1 gCm\({}^{-2}\)d\({}^{-1}\) in winter, and even 0 in some regions. Due to the minimum precipitation and temperature in winter, most of the vegetation is in the non-growing season, which makes the vegetation coverage the lowest, resulting in the lowest GPP throughout the year. Although crops are sown in the north, they grow slowly due to the low temperature. On the contrary, the GPP in southern Yunnan remains high even in winter (Figure 8d). The global climate has undergone significant changes in the past few decades, including frequent extreme climate disasters and sustained warming. Ecosystems and terrestrial carbon sinks will be significantly affected by climate change. On the other hand, GPP is greatly influenced by human activity [61].