# Impacts of Compound Hot-Dry Events on Vegetation Productivity over Northern East Asia
## 1 Introduction  
Since the advent of the Industrial Revolution, human-induced greenhouse gas emissions have instigated global climate change, consequently yielding a rise in the frequency and intensity of extreme weather events [1; 2; 3]. Owing to their sudden occurrence and inherent unpredictability, these extreme climate phenomena pose a significant peril to human society and the ecological environment [4; 5]. Vegetation plays a crucial role in the carbon cycle of terrestrial ecosystems. Gross primary productivity (GPP) is a critical indicator for measuring the products of vegetation photosynthesis [6; 7; 8]. However, in recent years, multiple significant extreme events have seriously impacted vegetation GPP. For instance, the extreme heat and drought in Europein 2003 caused a significant 30% reduction in GPP, almost offsetting four years of ecosystem carbon sequestration [9]; in the summer of 2012, a once-in-a-century drought in the central western United States suppressed crop and grassland growth, with grassland GPP decreasing by 29% [10]. Importantly, studies suggest that concurrent occurrences of heatwaves and droughts (i.e., compound hot-dry (CHD) events) may have greater impacts than individual extremes [11; 12; 13]. The prevalence of CHD events is projected to escalate across a majority of regions worldwide throughout the 21 st century [14; 15; 16; 17]. Considering the severe and devastating consequences associated with CHD events, it becomes imperative to enhance our comprehension and awareness of such events and evaluate their far-reaching impacts on ecosystems. Although previous studies have assessed the impacts of individual and concurrent extreme events associated with heatwaves and droughts on GPP [13; 18; 19], there remains a need to understand each process's quantitative contributions comprehensively. Additionally, the underlying physiological processes of vegetation during CHD events have yet to be fully elucidated. Understanding the roles of each climatic factor and accurately quantifying their effects are crucial for further advancements. One promising approach to unraveling the distinct contributions of individual event drivers involves constraining one (or more) of these drivers within a numerical model [20; 21; 22]. By manipulating specific meteorological variables such as precipitation or temperature in a land surface process model and subsequently observing the resulting changes in simulation outcomes, we can gain insights into the eco-hydrological dynamics under different meteorological conditions. This method enables us to explore the impact of specific drivers on the ecosystem and aids in revealing the underlying response mechanisms governing vegetation productivity during CHD events. Northern East Asia is highly sensitive to climate change and global warming, often experiencing severe impacts from heatwaves and droughts [23; 24; 25]. Additionally, this region has abundant vegetation resources and plays a critical role in regional and global carbon balances [26]. Therefore, northern East Asia is ideal for analyzing how vegetation responds to extreme events. To analyze the impact of CHD events, this study will utilize two key indicators: daily maximum temperature (T\({}_{\text{max}}\)) and the standardized precipitation index (SPI). A hot event will be defined as a period of at least three consecutive days with T\({}_{\text{max}}\) surpassing the 85 th percentile threshold. This threshold will be calculated using a 15-day moving window of daily maximum temperatures from 1979 to 2019 [3]. On the other hand, a dry event will be defined as a duration of at least three consecutive days where the SPI \(<-0.5\), following the methodology established by [PERSON] et al. [27]. The SPI is an index obtained by normalizing precipitation for a given time period, assuming that it follows the gamma probability distribution. This study will calculate daily SPI values from daily precipitation data utilizing the \"climate_index\" package in Python [28], with a time scale of 30 days.