## 5 Conclusions  
In this study, the bookkeeping and CASA models were combined to quantify the carbon budget caused by forest cover change, and the forest carbon sequestration change caused by CDHEs was estimated using GD and photosynthesis equations. The forest carbon dynamics caused by forest cover change and CDHEs in the two regions were comprehensively compared. On the one hand, forest cover change in both cities resulted in net forest carbon loss. The net carbon loss caused by forest cover change was 0.222 TgC and 1.293 TgC in Nanjing and Shaoguan, respectively. On the other hand, drought and heat events inhibited the efficiency of forest carbon sequestration. Forest carbon sinks showed a typical two-month lag response to drought (heat) events. The effect of CDHEs on forest carbon sinks is more significant than that of single extreme events, showing nonlinear enhancement and a two-factor interaction mode. We found that CDHEs dominated forest carbon loss in Shaoguan. CDHEs caused a net carbon loss of 0.186 TgC and 3.219 TgC in Nanjing and Shaoguan, respectively. In Nanjing, forest cover change dominated the forest carbon loss. CDHEs dominated forest carbon loss in Shaoguan. This study provided an evaluation framework for quantifying the effects of forest cover change and CDHEs on forest carbon dynamics. This finding contributes to a deeper understanding of the mechanisms of climate change's impact on forest carbon sinks. It shows that relevant departments need to consider the effects of forest cover change and extreme climate events to protect and enhance the carbon sink function of forests more effectively. **Supplementary Materials:** The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/f15112048/s1](https://www.mdpi.com/article/10.3390/f15112048/s1)]([https://www.mdpi.com/article/10.3390/f15112048/s1](https://www.mdpi.com/article/10.3390/f15112048/s1)), Table S1: Vegetation carbon density (unit: TC per ha); Table S2: Soil carbon density (0-20 cm, unit: TC per ha); Table S3: Parameters of bookkeeping model; Table S4: Interaction between explanatory variables; Table S5: Forest cover change area in Shaoguan City from 2000 to 2022 (unit: ha); Table S6: Forest cover change area in Nanjing City from 2000 to 2022 (unit: ha); Table S7: Forest cover change type; Figure S1: Spatial distribution of combined drought and heat events (CDHEs) in Shaoguan (a) and Nanjing (b). **Author Contributions:** C.G. : writing--original draft. T.W. : validation. W.S. : writing--review and editing, supervision, and conceptualization. Z.T. : investigation. X.S. : investigation. J.H. : writing--review and editing and data curation. T.H. : resources. W.G. : methodology. C.H. : conceptualization. All authors have read and agreed to the published version of the manuscript. **Funding:** Our work was jointly funded or supported by the National Natural Science Foundation of China (32371878, 32001251), the Youth Science and Technology Talent Lifting Project of Jiangsu Province, the Natural Science Foundation of Jiangsu Province (BK20200781), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). Special thanks to the Guangdong Provincial Center for Forest Resources Monitoring for providing help with field survey data. **Data Availability Statement:** Data will be made available on request. **Conflicts of Interest:** The authors declare no conflicts of interest. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years. _Remote Sens. Environ._**2019**, _233_, 111383. [CrossRef]
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