## 1 Introduction  
Terrestrial ecosystems are the main carbon pools at the surface, with a huge potential for sequestration, and are critical to achieving carbon neutrality [1]. Among these, forest and grassland ecosystems are particularly significant, as they play an important role in carbon sequestration through the photosynthetic activity of green plants [2]. The carbon sequestration capacity of forest and grassland ecosystems is typically quantified in terms of productivity, which is further categorized into GPP (gross primary productivity), NPP (net primary productivity), and NEP (net ecosystem productivity). These categories differ in how they describe the carbon sequestration process: GPP refers to the total amount of organic carbon fixed by green plants via photosynthesis, NPP accounts for the portion of GPP remaining after subtracting autotrophic respiration by green plants, and NEP represents the part of NPP left after deducting heterotrophic respiration within the ecosystem [3]. Accurately estimating the productivity of forest and grassland ecosystems has been a central focus in both forestry and ecological research [4]. Model simulation is a primary tool for estimating the productivity of forest and grassland ecosystems. These simulations can be categorized into statistical models, light-use efficiency models, and ecosystem process models, based on the underlying modeling principles [5]. Among these, the ecosystem process model has become the mainstream tool for estimating forest ecosystem productivity due to its detailed representation of carbon, nitrogen, and water cycling processes within forest and grassland ecosystems [6]. The Biome-BGC model, a classic example of an ecosystem process model, has been widely applied across various regions in China because of its robust mechanistic properties. [PERSON] and [PERSON] used the Biome-BGC model to simulate the NPP of forests in Xinjiang and Northeast China, respectively, focusing on the degree of response of forest ecosystem NPP to climate change [7; 8]. [PERSON] used the Biome-BGC model to simulate the NPP of mangrove forests in the coastal region of southern China, and [PERSON] used the Biome-BGC model to explore the relationship between climate change and forest water use efficiency in the Qianyanzhou region of southern China [9; 10]. In addition, some researchers focused on parameter sensitivity analysis studies of the Biome-BGC model when applied in China [11; 12; 13], and the studies of other Chinese researchers focused on remote sensing data assimilation and improvement of the Biome-BGC model [12; 14; 15; 16; 17]. All of these studies mentioned above used the Biome-BGC model as a tool to simulate the carbon and water cycles of ecosystems at small scales in specific regions of China, but there are no studies focusing on the applicability of the Biome-BGC model in China as a whole for the time being. The Biome-BGC model originated from the Forest-BGC model, which was initially developed to simulate the climate and forest types of the western United States [18]. However, China's vast land area, diverse topography, and complex vegetation and climate types differ significantly from those of the western United States [19]. Whether the Biome-BGC model can be effectively applied to various climate and vegetation types across China has not been comprehensively evaluated. If the mechanistic processes underlying the Biome-BGC model are not suitable for some regions in China, it raises questions about the validity of productivity simulations conducted by Chinese researchers using the model in those regions. To evaluate the applicability of the Biome-BGC model across different regions of China and determine the spatial patterns of its applicability, this study selected 10 typical forest and grassland ecosystems in China, each equipped with flux stations (eddy covariance systems). These flux stations can directly measure the GPP and NEP of the ecosystems, and the flux observation data can be used as direct validation data for the model simulation results [12].