## 1 Introduction  
Land surface processes connect the atmosphere, biosphere, lithosphere, and hydrosphere, regulating the dynamic balance of the Earth system through exchanges of energy, water, and carbon. These processes significantly influence regional and global climate systems, ecosystem dynamics, and service functions [1, 2]. The study of water and carbon fluxes typically involve measuring two key components of terrestrial vegetation: evapotranspiration (ET) and gross primary productivity (GPP) [3]. ET is a central component of the surface energy balance; it not only regulates regional water resources but serves as a core process connecting hydrological and energy cycles [4; 5]. GPP quantifies the capacity of green plants to fix carbon through photosynthesis, representing the starting point of the terrestrial carbon cycle and directly determining the ecosystem's carbon sequestration potential [5; 6]. Land surface models (LSMs) have undergone significant advancements in recent decades and have become indispensable tools for accurately simulating and predicting water and carbon cycles in terrestrial ecosystems [7; 8]. Numerous studies have utilized a variety of LSMs, including the Community Land Model [9; 10], the community Noah LSM with multiparameterization options (Noah-MP) [11], and the Joint UK Land Environment Simulator [12]. These models employ complex parameterization schemes to describe vegetation physiology, soil hydrology, and biogeochemical processes, aiming to capture the dynamic changes in water and carbon fluxes. Extensive simulations and evaluations of ET and GPP have been conducted at global or regional scales. The results indicate that LSMs can, to some extent, capture the main spatiotemporal variation characteristics of ET and GPP [13; 14]; many models can reasonably reproduce the diurnal and seasonal cycles of carbon fluxes and respond to interannual fluctuations [13], but accurately simulating interannual variability remains somewhat difficult [14]. [PERSON] et al. [10] and [PERSON] et al. [15] improved the water and carbon flux simulation capabilities of the Community Land Model version 5 by refining modules such as hydrology, snow processes, plant hydraulics, and nitrogen cycling, and by introducing more comprehensive land management descriptions. The Noah-MP model, developed based on the Noah LSM, has garnered widespread attention in water and carbon flux simulation research due to its flexible multiparameterization options, which allow users to select the most suitable scheme combinations for different physical processes [11]. Studies have evaluated the performance of Noah-MP at global and regional scales. For instance, [PERSON] et al. [16] assessed the performance of multiple hydrological parameterization schemes in Noah-MP over the continental United States. To further enhance the simulation accuracy of Noah-MP, many studies have also focused on improving its key physical processes. For example, applying eco-evolutionary optimality (EEO) principles to the simulation of photosynthesis and vegetation respiration has shown potential in reducing GPP simulation biases [17]. Data assimilation techniques, such as assimilating satellite-derived leaf area index (LAI) or surface soil moisture, have also been widely used to constrain Noah-MP's state variables, thereby improving ET and GPP estimations [18]. Despite significant progress in evaluating LSM simulations of water and carbon fluxes, challenges remain. Firstly, although the Noah-MP model has been widely applied and evaluated, systematic and comprehensive assessments of its latest version are still relatively scarce at the global scale. Noah-MP 5.0, which features code modernization and updated physics, has not yet been thoroughly evaluated across most vegetation types and multiple temporal scales [19; 20]. Most evaluations have focused on specific regions, limited vegetation types, or earlier model versions.