### Data Acquisition  
The grassland ecosystem boundary of China in this study was extracted from GlobeLand30 2020 [23].  
The data used to calculate the NEP of the CGE included land surface water index (LSWI), photosynthetically active radiation (PAR), enhanced vegetation index (EVI) temperature and precipitation. LSWI was calculated with MOD09A1 V6 surface reflectance data on the GEE platform with reference to the study of [PERSON] et al. [24]. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite has visible, near-infrared and shortwave infrared bands, and the principle of this method is to take into account that the absorption rate of vegetation in the near-infrared band is low, while that in the short infrared band is high.  
PAR is the MCD18A2 product, and it represents a part of the solar radiation spectrum from 0.4 to 0.7 \(\upmu\)m that is absorbed, transferred and stored within ecosystems [25]. The data were generated by the Terra and Aqua MODIS combined surface radiation algorithm. The algorithm starts by estimating surface reflectance from a set of multi-temporal data inputs and evaluates the PAR flux for each of them.  
EVI is provided by MOD13A2 V6 products to select the best available pixel values from all collections over a 16-day period [26]. It is calculated by bidirectional surface  
Figure 1: Location of the study area (the boundaries, names and designations are for illustrative purposes only and do not represent an official endorsement).  
reflectance with atmospheric correction and screened for water, clouds, heavy aerosol and cloud shadow. A blue band was used to remove EVI smoke and sub-pixel thin clouds caused by residual air pollution. The standards for use were low clouds, low viewing angles and highest EVI values.  
Temperature data were from the National Earth System Science Data Center ([[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn)), accessed on 15 March 2020), which is based on monthly average temperature data from 824 benchmark meteorological stations on the ground in China from 2010 to 2020. The monthly grid data with a horizontal resolution of \(1\times 1\) km in China were generated by spatial interpolation using a DEM with a resolution of \(1\)km in China as a covariable. After data acquisition, the monthly temperature was superimposed and averaged to obtain the annual average temperature dataset.  
The meteorological data used in this study were temperature and precipitation. The temperature data were consistent with the temperature data for calculating NEP. Precipitation data were from the National Earth System Science Data Center. ([[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn)), accessed on 15 March 2020) These data were generated for the China area by downscaling the delta spatial downscaling scheme based on the global 0.5\({}^{\circ}\) climate data released by CRU and the global high-resolution climate data released by WorldClim. Data from 496 independent meteorological observation points were used to verify the reliability of the results.  
The vector data of Chinese administrative regionalization used in this study were from the National administrative regionalization information query platform. ([[http://xzqh.mca.gov.cn/map](http://xzqh.mca.gov.cn/map)]([http://xzqh.mca.gov.cn/map](http://xzqh.mca.gov.cn/map)), accessed on 15 March 2020). The data are mainly used for statistical analysis and horizontal comparison among administrative divisions.  
In this study, all obtained data were preprocessed with ArcGIS 10.5 software for the unified coordinate system and spatial resolution. The data coordinate system was converted to GCS_WGS_1984. The datum was D_WGS_1984, the prime meridian was Greenwich and the angular unit was degrees. The spatial resolution of all raster data was \(500\times 500\) m. The null value region was spatially interpolated or assigned a value of 0 depending on the type and meaning of the data.