## 1 Introduction
Currently, methods for adjusting dynamic LUEmax parameters can be divided into the following four categories: (1) Methods based on vegetation indices [25,26]--Various vegetation indices and their corresponding parameter adjustment algorithms constitute the vegetation-index-based LUEmax parameter adjustment method [25]. For example, the Leaf Area Index (LAI) can be obtained from satellite data, and then the LUEmax can be derived toimprove crop biomass estimation [6]. (2) Statistics-based methods [27], such as establishing a model through machine learning, can improve the estimation of gross primary productivity in global biomes [28]. (3) Model-based back-calculation methods--for example, the LUEmax of different types of vegetation can be estimated through the model combined with time-series remote sensing data and ground measurements [2]. (4) Utilization of light-response equation-based methods [29], such as right-angle hyperbolic equations [30]. The principles of the above four methods are distinct, and each can express the dynamic characteristics of LUEmax. Based on site flux data, the fourth method is more effective for explaining the dynamics of the LUEmax. Moreover, it enables more accurate cross-comparisons of LUEmax among various vegetation types under different climatic conditions. The LUEmax can exhibit large variability within individual biomes [31; 32]. The influence mechanism of LUEmax involves physiological factors and environmental factors [33], which mainly include temperature, vapor pressure deficit (VPD), and radiation variability [34], among other factors. These factors have varying degrees of influence on the LUEmax of vegetation. In China's arid and semi-arid ecosystems dominated by grasslands and deserts, the LUEmax exhibited a significant positive correlation with precipitation levels and LAI [35]. The LUEmax is enhanced significantly in tundra ecosystems with the increase in the Enhanced Vegetation Index (EVI) [36]. Vegetation in arid ecosystems exhibits heightened sensitivity to soil moisture fluctuations [37]. Recent studies demonstrate that Bayesian-derived LUEmax values exhibit a significant linear, positive correlation with LAI, particularly pronounced in deciduous plant functional types [27]. Most existing studies ignore the important impacts of local climatic factors and geographical conditions on estimating LUEmax. There are differences in temperature, precipitation, light, and soil conditions across various climate zones, such as tropical, temperate, and polar zones. However, the variation in the LUEmax in different climate zones has not been thoroughly investigated. Temperate forests exhibit significantly higher LUEmax values than subtropical and tropical forest ecosystems [38]. These studies indicate that there is still large uncertainty in the regulatory factors of the LUEmax parameter. Therefore, exploring the key factors influencing the LUEmax will enhance its accuracy, which is important for understanding regional and global vegetation productivity. In the context of global climate change, analyzing the regular variation patterns in LUEmax values during vegetation growth and exploring their driving mechanisms are important tasks. Providing a theoretical basis for accurately assessing the uncertainties in LUE modeling processes and improving their precision has become a pressing issue in terrestrial ecosystem carbon cycle research. Therefore, based on previous studies, this study aimed to (1) evaluate differences in LUEmax values across multiple vegetation ecosystems in different climate types and (2) analyze the main regulators of the LUEmax in multiple vegetation ecosystems under different climate types.