## 1 Introduction  
Water use efficiency (WUE) is a crucial ecological indicator for quantifying terrestrial carbon-water coupling processes [1, 2]. It is defined as the total amount of organic carbon fixed per unit mass of water consumed by vegetation during photosynthesis at the leaf scale [3, 4]. At higher ecosystem scales, WUE is expressed as the ratio of gross primary productivity (GPP) to evapotranspiration (ET) [5, 6, 7]. Currently, the mechanisms by which environmental factors regulate leaf-level carbon-water coupling processes through plant physiological processes (e.g., stomatal conductance and community structure) have been extensively studied [8; 9], but a large knowledge gap remains in understanding the influence of environmental factors on ecosystem WUE variations [10; 11]. Understanding the response of WUE to climate and its driving factors is crucial for formulating ecosystem management strategies to mitigate and adapt to climate change [12]. Numerous studies have investigated the response of WUE changes to climate using various methods, such as eddy covariance (EC) observations, process-based models, and satellite remote sensing [13; 14; 15; 16]. Vegetation carbon flux, water vapor flux, and energy flux are commonly measured in the field using the EC technique, which allows the estimation of GPP and ET [17; 18]. However, due to the limited observational coverage of EC flux towers, two widely used approaches have been developed to estimate ecosystem WUE accurately on larger scales, such as the global scale. The first approach involves expanding available EC observational data to broader areas through machine learning or statistical models [19; 20]. The second approach uses satellite remote sensing-derived data products as inputs to ecosystem models, applying various algorithms to quantify WUE [21; 22; 23]. Among these two approaches, ecosystem models and remote sensing-based algorithms are more effective in capturing the dynamic changes in WUE. As a result, ecosystem WUE developed from these models (e.g., PML, MODIS) has been widely used to study the driving factors behind WUE variations in different regions [24; 25; 22]. The coupling of carbon and water cycles in vegetation suggests that environmental disturbances affecting one component of WUE (GPP or ET) may influence the other [26]. Previous studies have suggested that ecosystem WUE variations are influenced by multiple factors (e.g., precipitation, air temperature, radiation, atmospheric CO\({}_{2}\) concentrations, soil moisture, and vapor pressure deficit (VPD)) [27; 28]. Moreover, the response mechanisms of WUE variations to these factors exhibit differences across different regions [29; 30; 31]. [PERSON] et al. [32] used the improved remote sensing-driven analytical model in humid regional forest ecosystems of China, ref. [32] highlighting that the change in CO\({}_{2}\) and VPD can directly affect the variation of WUE at an annual scale. Furthermore, the increase in CO\({}_{2}\) concentrations has rapidly enhanced carbon sequestration capacity, known as the \"fertilization effect,\" while the rise in VPD has intensified ET in terrestrial vegetation. As a result, the combined effects of changes in CO\({}_{2}\) and VPD have led to an overall increase in annual water use efficiency (WUE) in subtropical forests. [PERSON] et al. [33] indicated that climate change, particularly variations in precipitation and temperature, can statistically explain approximately half of the inter-annual variation in WUE across the North China region. It found that the changes in ET and GPP, i.e., increased ET and decreased GPP, are not consistent when the annual precipitation exceeds 510 mm and annual air temperature exceeds 13 \({}^{\circ}\)C in North China for semi-humid regions. In the arid and semi-arid regions of China, the WUE of grasslands in central Inner Mongolia exhibits a strong positive response to precipitation. In contrast, the WUE of forests in the relatively humid northeast of Inner Mongolia shows a strong negative response to soil moisture [34].