## 5 Conclusions  
In order to validate the accuracy of carbon flux predictions from the GCFD generated by a machine learning model and evaluate their capacity to accurately depict the actual spatial distribution of carbon fluxes, this study conducted a comparative analysis of three types of carbon flux products: remote sensing, ecosystem model, and machine learning products. The comparison was conducted on both the site scale and the global scale. Furthermore, this study examined the overall patterns of variation in carbon fluxes by analyzing their time series and spatial distribution. Additionally, an attribution analysis was performed, taking into account significant factors affecting carbon fluxes. Our conclusions are as follows. First, on the site scale, among the products, the GCFD product shows the closest agreement with site observations in terms of predicted carbon fluxes. Second, the spatial distribution patterns of carbon fluxes are similar across all three product types, with the smallest bias observed between the GCFD and machine learning products. Third, regarding temporal trends, GPP and RECO exhibit consistent patterns, while NEE shows an opposite trend. On the global scale, GPP values exhibit an overall increasing trend, with the tropics experiencing the most significant increase. Conversely, the polar regions display the lowest GPP values. Fourth, vegetation-related variables such as FAPAR and LAI play crucial roles in influencing the spatial and temporal variations in carbon fluxes. The temporal trends and spatial distribution of carbon fluxes align with the characteristics of FAPAR and LAI. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Continent** & **Number** & **Climate Zone** & **Number** & **Land Cover Type** & **Number** \\ \hline Asia & 18 & continental & 131 & forest & 65 \\ Africa & 8 & dry & 29 & grassland & 95 \\ North America & 113 & Mediterranean & 33 & shrubland & 20 \\ South America & 8 & oceanic & 21 & cropland & 15 \\ Europe & 109 & polar & 23 & savannah & 74 \\ Oceania & 24 & subtropical & 31 & unvegetated & 11 \\  & & tropical & 19 & & \\ \hline \end{tabular}
\end{table} 
Table 2: Number of sites in different continents, climate zones, and land cover types. **Author Contributions:** [PERSON]. ([PERSON]) conceived the research and secured funding for the research. [PERSON]. ([PERSON]) and [PERSON] designed the experiment. [PERSON], [PERSON] ([PERSON]), [PERSON], [PERSON] ([PERSON]) and [PERSON] performed the analyses. [PERSON] wrote the first draft of the manuscript. [PERSON] and [PERSON]. ([PERSON]) conducted the research. [PERSON]. ([PERSON]), [PERSON], [PERSON] ([PERSON]), [PERSON], [PERSON], [PERSON] ([PERSON]) and [PERSON] reviewed and edited the manuscript before submission. All authors have read and agreed to the published version of the manuscript. **Funding:** The study was partially supported by the National Natural Science Foundation of China (42088101, 42375144, U1811464, 41975122, 42075160, 4227515 and 42205149), the Guangdong Basic and Applied Basic Research Foundation (2021B0301030007, 2021A1515110215 and 2023A1515011996), and the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (311020008). **Data Availability Statement:** FLUXNET2015 and FLUXNET-CH4 are available at [[https://fluxnet.org/](https://fluxnet.org/)]([https://fluxnet.org/](https://fluxnet.org/)) (accessed on 7 June 2023). Drought-2018 is available at [[https://www.icos-cp.eu/data-products/YVR0-4898](https://www.icos-cp.eu/data-products/YVR0-4898)]([https://www.icos-cp.eu/data-products/YVR0-4898](https://www.icos-cp.eu/data-products/YVR0-4898)) (accessed on 7 June 2023). The GCFD can be accessed at [[https://doi.org/10.11888](https://doi.org/10.11888)]([https://doi.org/10.11888](https://doi.org/10.11888)) /Terre.tpdc.300009 (accessed on 21 June 2023). The FLUXCOM product is available at [[http://fluxcom.org/](http://fluxcom.org/)]([http://fluxcom.org/](http://fluxcom.org/)) (accessed on 21 June 2023). Products by Zeng et al.