## 1 Introduction
As both domestic and international scholars deepen their research on water-use efficiency (WUE), a growing range of methods for exploring its influencing factors continues to emerge. Most existing studies have utilized methods such as simple linear regression, the Mann-Kendall method [26; 27; 28], and the coefficient of variation to examine the temporal and spatial variations of WUE. However, these methods do not account for the effects of the seasonal and residual terms of WUE on the trend of its changes. Seasonal trend decomposition using LOESS (STL) applies robust locally weighted regression as a smoothing method and decomposes time-series data into three components: the seasonal, trend, and residual terms. These terms help mitigate the impact of seasonal and residual terms on the trend component [29; 30]. Moreover, the predominant methods used to attribute WUE influencing factors include multiple linear regression, Pearson correlation analysis, least squares methods, and stepwise regression analysis [31; 32; 33]. However, these traditional linear methods struggle to capture the nonlinear relationships between WUEc and its influencing factors and overlook the coupling effects between these factors. Machine learning methods can more accurately address the nonlinear relationships between influencing factors. Furthermore, this study integrates the results from the machine learning model with ArcGIS software (version 10.8.2) to present the model's outcomes in a spatial format, thereby better reflecting the potential relationship between WUEc and its influencing factors [34, 35, 36]. Therefore, this study employed the STL decomposition method to analyze the temporal trend of WUEc and used machine learning methods to quantitatively assess the contributions of meteorological and vegetation factors to WUEc. Over the past five decades, global drought has intensified: 27.9% of the land surface has become significantly drier, the area classified as arid has expanded by approximately 10 million km\({}^{2}\), four billion people now face at least one month of water shortage each year, and annual global freshwater deficits are projected to exceed 3200 km\({}^{3}\) by 2050 [37]. However, within research on typical irrigated regions heavily dependent on irrigation water, large-scale assessments of WUEc integrating remote sensing models and machine learning techniques remain scarce, and the roles of meteorological and vegetation factors in driving WUEc dynamics have received limited attention. To address this gap, the present study uses Xinjiang--a paradigmatic irrigated agricultural region--as a case study to systematically characterize the spatiotemporal evolution of WUEc and quantify its primary drivers. Quantitative evaluation of long-term WUEc changes is essential for improving water-use efficiency in Xinjiang's irrigated agriculture and for achieving water-saving, high-yield, and high-efficiency production [38, 39]. In summary, this study employs STL decomposition, XGBoost-SHAP machine learning models, and other methods to systematically investigate the dynamic changes in WUEc in Xinjiang under environmental changes and identify its key driving factors. The goal is to clarify how environmental factors influence the water-carbon cycle in a typical irrigated agricultural region. The study is structured into four parts: (1) analyzing the spatiotemporal variation characteristics of WUEc in Xinjiang from 2002 to 2022; (2) exploring the sustainability and future evolutionary trends of WUEc in Xinjiang during the same period; (3) clarifying the response relationships between WUEc and both meteorological and vegetation factors in Xinjiang from 2002 to 2022, along with the main driving factors; and (4) investigating the nonlinear relationships between WUEc and meteorological and vegetation factors in Xinjiang. This study provides a scientific foundation for the efficient allocation of agricultural water and soil resources in the region.