## 1 Introduction  
The Tibetan Plateau (TP) is the highest plateau in the world with an average altitude of about 4000 m and is known as the \"Roof of the World\" [1]. In recent decades, temperature increased significantly on the TP, and TP warming is occurring twice as fast as global warming [2]. The unique high and cold ecosystem of the TP has a simple ecological structure and is very vulnerable to climate change [3]. Net Primary Productivity (NPP) of vegetation is a key ecological indicator for detecting C fluxes, and it refers to the amount of organic carbon produced by plant communities in an ecosystem per unit of time and area [4]. NPP is the remaining portion of the total amount of organic matter fixed by photosynthesis minus the amount consumed by its own respiration. It is an important indicator of ecosystem health and ecological balance [5], as well as global carbon balance [6]. Many models have been conducted to calculate and analyze the change of NPP and its influencing factors. The field measurement is the direct method to investigate the NPP. Given the harsh environmental conditions of the TP, field measurement data have limited spatial coverage; hence it is often used as the verification data. Statistical models mainly include traditional climate correlation models and remote sensing data-driven models [7]. Statistical models were simple in structure [8, 9, 10], but these models lack the theoretical foundation of plant copphysiology as well as the ability to illustrate how the meteorological environment interacts with the ecosystem [11]. The potential of the machine learning method has been widely acknowledged in the field of ecological science in recent years [12; 13]. The machine learning approaches (e.g., Random Forest, RF; Support Vector Machines, SVM; k-Nearest Neighbor, KNN) have been successfully used to derive carbon fluxes using the fluxnet measurements and satellite-derived variables [14; 15; 16; 17]. In addition, machine learning algorithms such as gradient boosting (GB) [18], extreme gradient boosting (XGB) [19], and light gradient boosting machine (LGBM) [20], have achieved good performance in carbon flux retrieval. These algorithms are under the gradient boosting framework and can provide parallel tree boosting to derive carbon fluxes in a fast and accurate way. The process-based models combined with the physiological characteristics of plant growth and development were able to simulate the functional processes within the ecosystem and construct the interaction process between the plant interior and external meteorological conditions. Process-based models are represented by the CLM model [21; 22], Biome-BGC model [23], and TEM model [24], etc. The Community Land Model (CLM) is the most developed and widely used land surface model in the world and has been called the largest terrestrial biosphere model [25; 26], also suitable for the Tibetan Plateau. [PERSON] et al. [27] used the CLM4.5 model to simulate the spatial and temporal variations of water use efficiency (WUE) in the Tibetan Plateau, and its response to environmental factors found that WUE showed an increasing trend and its main influencing factors were the CO\({}_{2}\) concentration and temperature; [PERSON] et al. [28] used the CLM model assessed NPP and a variety of water-related ecosystem services in the Qinghai Plateau region from 2000 to 2018, revealing that vegetation dominated the relative locations of NPP thresholds. Precipitation, temperature, nitrogen deposition, carbon dioxide concentration, and many other factors can affect the NPP of vegetation [29]. [PERSON] et al. [30] used correlation analysis combined with the Thorthwaite Memorial model to analyze the changes in the spatial distribution pattern of NPP in China from 2000 to 2017. They suggested that NPP is mainly affected by temperature in the eastern Tibetan Plateau. [PERSON] et al. [31] used the CASA model and biased correlation analysis to analyze the NPP change and the influence factors on the Tibetan Plateau.