## 1 Introduction  
Water use efficiency (WUE) in an ecosystem is the ratio of gross primary productivity (GPP) to evapotranspiration (ET), reflecting the balance between carbon storage and water consumption within the system [1]. As a key indicator for measuring ecological sustainability, WUE reflects the process of carbon-water exchange between terrestrial ecosystems and the atmosphere, and can quantify the amount of water consumed by ecosystems during carbon assimilation [2, 3]. WUE plays a crucial role in the global ecosystem's functionality, ecological services, and its feedback to climate change [4, 5]. Therefore, a deeper understanding of the spatiotemporal variations in WUE and its driving factors can contribute to promoting regional sustainable development and the effective use of water resources. Traditional methods for obtaining WUE include field measurements, data mining, and process-based model simulations. Among these, field measurements require significant human and material resources, making them less efficient and suitable only for smaller study areas [6, 7]. Data mining and process models are limited by parameters and are highly dependent on input data, which may lead to biased results [8, 9]. With the development of remote sensing technologies and inversion methods, it is now easier to obtain high-precision GPP and ET products. Currently, remote sensing data is one of the mainstream methods for obtaining GPP and ET on a larger scale, and using remote sensing data to estimate WUE has been widely applied in various ecological and environmental studies [10; 11; 12]. Examples include small-scale studies on agricultural land [13], on grasslands [14], and in forests [15]; medium-scale studies in cities such as Beijing [16], North China [17], and Xinjiang [18]; and large-scale studies across regions like India [19], the Northern Hemisphere [20], and globally [21]. For the analysis of the driving factors of WUE [PERSON], et al. [22] used regression methods to analyze the impacts of climate change, vegetation growth, and CO\({}_{2}\) concentrations on the interannual variations in WUE. [PERSON] et al. [23] explored the effects of industrial structure and urban environments in the Yangtze River basin on WUE using geographically weighted regression. [PERSON] et al. [24] employed Spearman correlation to analyze the drought response and dominant ecosystem processes of WUE using four different WUE products. [PERSON] et al. [25] used geographic detectors to study the influence of meteorological factors on WUE in the northernmost region of the Yellow River, finding that the interaction of multiple climatic factors had a stronger effect on WUE than any single factor. [PERSON] et al. [26] explored the impact of CO\({}_{2}\) fertilization on WUE in the Tibetan Plateau region using a land biosphere model, concluding that CO\({}_{2}\) fertilization had a more significant effect on WUE in the driest years compared to the annual average. Most of these traditional methods for analyzing driving factors focus on correlation or simple regression analysis and have shown that the spatiotemporal variations in various driving factors have a substantial impact on WUE. However, the impact of each driving factor on WUE dynamics still requires further investigation. The changes in WUE cannot be explained solely by the strength of the correlation with the driving factors; the trends, including both the magnitude and the direction of change, must also be taken into account. The contribution of each driving factor to the WUE trend involves not only the relationship's effect but also the direction and extent of the driving factor's trend. Machine learning models such as XGBoost can efficiently establish nonlinear relationships between driving factors and WUE, providing a more reasonable representation of actual trends.