###### Abstract  
Compound drought and heat events (CDHEs) and forest cover change influence regional forest carbon dynamics. Changes in regional vegetation biomass and soil carbon storage induced by forest cover change often exhibit considerable uncertainty, and previous research on the impacts of CDHEs on forest carbon dynamics is limited. To accurately quantify the specific effects of forest cover change and CDHEs on forest carbon dynamics in different regions, we employed a combined algorithm of the Carnegie-Ames-Stanford Approach (CASA) and bookkeeping empirical models to examine the impact of regional forest cover changes on forest carbon dynamics during 2000-2022 in Nanjing and Shaoguan, Southern China. Using the Geographical Detector model, we then analyzed the effects of CDHEs on forest carbon dynamics. Next, we used the photosynthesis equation and the optimal response time of forests to drought (heat) events to calculate the changes in forest carbon sequestration caused by CDHEs in both regions during 2000-2022. The results indicated that afforestation and deforestation led to +0.269 TgC and +1.509 TgC of carbon sequestration and 0.491 TgC and 2.802 TgC of carbon emissions in Nanjing and Shaoguan, respectively. The overall effects of CDHEs on the change in forest carbon sequestration were manifested as net carbon loss. In Nanjing, the net carbon loss caused by CDHEs (0.186 TgC) was lower than the loss due to forest cover change (0.222 TgC). In Shaoguan, the net forest carbon loss caused by CDHEs (3.219 TgC) was much more significant than that caused by forest cover change (1.293 TgC). This study demonstrated that forest carbon dynamics are dominated by different factors in different regions, which provides a scientific basis for local governments to formulate targeted forest management policies. compound drought and heat events; forest cover change; bookkeeping model; geographical detector; carbon storage +
Footnote †: journal: Medical-frequent afforestation and deforestation activities change the land cover and affect the carbon balance, which deserves attention [9; 10]. Therefore, precisely quantifying the effects of forest cover change and compound drought and heat events (CDHEs) on forest carbon dynamics is vital for understanding how human activities and extreme climate events influence the forest carbon balance. Over the past few decades, numerous studies have been undertaken to enhance our comprehension of the effects of CDHEs on forest carbon dynamics. For instance, [PERSON] et al. [11] found that during the 2022 CDHEs in the Yangtze River Basin, low soil moisture (SM) and a high vapor pressure deficit (VPD) were the primary factors contributing to reduced forest productivity. [PERSON] et al. [12] showed that high temperatures significantly impacted gross primary productivity (GPP) reduction, followed by low soil moisture. VPD contributed the least through multiple linear regression analysis of the impact of various indicators on GPP during July-October 2022. Nevertheless, most research investigating the effects of CDHEs on forest carbon dynamics relies on correlation, regression, and trend analysis methods. These studies presume notable linear relationships between related factors and vegetation growth [13; 14; 15]. Yet, the responses of terrestrial vegetation to various influences can be intricate, and relying solely on linear statistical methods may not suffice to portray these relationships accurately [13]. While copula-based models have been used to study the interaction of CDHEs and vegetation, they may not precisely distinguish the specific interactions of CDHEs [13; 14; 15]. Geographical Detector (GD) is a model for exploring spatial heterogeneity in vegetation and its relevant factors, pinpointing the importance and degree of diverse driving factors (meteorological, geographical, and human activities, etc.) and elucidating the interplay between these factors and vegetation [16; 17].