## 5 Conclusions  
By evaluating the carbon balance of Yunnan forest ecosystems from the period 2020-2060 based on the individual tree-based model FORCCHN, we have come to the following conclusions: First, both the GPP and ER of forest ecosystems showed an increasing trend under different climate change scenarios, with NPP peaking in the 2050s. Second, forest ecosystems in southwest China were still a carbon sink, but NEP will peak and begin to decline around the 2030s. Third, the forest NEP under the SSP1-2.6 scenario was higher than that under SSP2-4.5 and SSP5-8.5 in 2025-2035 and 2043-2058, and its carbon sequestration potential under the SSP1-2.6 scenario had a smaller CV than that under the other climate change scenarios. Forest carbon sequestration rates in southern China showed a decreasing trend, and a slight increasing trend in central and northwestern China. Finally, GPP, NPP, and ER were significantly and positively correlated with temperature and insignificantly correlated with precipitation, and rising temperatures will have a negative and destabilizing effect on forest carbon sinks. The inhibitory effect on carbon sequestration increased significantly with increasing temperature. Based on spatial and temporal carbon sequestration and the variability and trend characteristics of forests under different scenarios, we proposed that a series of management measures could be implemented to improve the carbon sequestration capacity of forests in southwest China. After the 2030s, forest management measures can be carried out for forests in the southern region under the SSP1-2.6 scenario, while forest governance is recommended for Dali and Chuxiong in the central region under the SSP2-4.5 and SSP5-8.5 scenarios. These measures included fire prevention, pest and disease prevention, nurturing young forests, thinning, fertilization, and management of dead wood to further enhance the forest's ability to sequester CO\({}_{2}\) from the atmosphere. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs15051442/s1](https://www.mdpi.com/article/10.3390/rs15051442/s1)]([https://www.mdpi.com/article/10.3390/rs15051442/s1](https://www.mdpi.com/article/10.3390/rs15051442/s1)), Table S1: Physiological and ecological parameters of the nine basic types of trees in the model. Conceptualization, X.Y. and F.L. ; methodology, X.Y. and F.L. ; software, F.L. ; validation, F.L. ; formal analysis, F.L. ; data curation, F.L. ; writing--original draft preparation, F.L. ; writing--review and editing, F.L., Y.S. and X.Y. ; visualization, F.L. ; supervision, X.Y. ; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript. This research was supported by the State Key Laboratory of Earth Surface Processes and Resource Ecology (2022-GS-01) and the National Key Research and Development Program of China (2019 YFA0606904 & 2018 YFC509003). CIMP6 products are from the website [[https://esgf-node.llnl.gov/projects/cmip6/](https://esgf-node.llnl.gov/projects/cmip6/)]([https://esgf-node.llnl.gov/projects/cmip6/](https://esgf-node.llnl.gov/projects/cmip6/)), accessed on 2 July 2022; soil data are available at [[http://www.issas.ac.cn/](http://www.issas.ac.cn/)]([http://www.issas.ac.cn/](http://www.issas.ac.cn/)), accessed on 6 June 2022; the ChinaFLUX dataset can be downloaded at [[http://www.chinaflux.org/](http://www.chinaflux.org/)]([http://www.chinaflux.org/](http://www.chinaflux.org/)), accessed on 16 May 2021; CO\({}_{2}\) data are available at [[https://climate.nasa.gov/vital-signs/carbon-dioxide/](https://climate.nasa.gov/vital-signs/carbon-dioxide/)]([https://climate.nasa.gov/vital-signs/carbon-dioxide/](https://climate.nasa.gov/vital-signs/carbon-dioxide/)), accessed on 10 October 2022; MODIS C6 LAI data are available at [[http://globalchange.bnu.edu.cn/research/laiv6](http://globalchange.bnu.edu.cn/research/laiv6)]([http://globalchange.bnu.edu.cn/research/laiv6](http://globalchange.bnu.edu.cn/research/laiv6)), accessed on 2 October 2022.