## 2 Materials and Methods
### GPP Data  
The Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a model comparison program aimed at developing globally gridded estimates of carbon, energy, and hydrological fluxes among different models of land and atmosphere [12]. To ensure consistent and comparable model results, the MsTMIP has assembled benchmark reference driver data sets, including standard weather drivers, remotely sensed phenology, biome classification, and land-use history to provide GPP output products in historical periods [13]. In the newly released model simulation results ([[https://daac.ornl.gov/NACP/guides/NACP_MsTMIP_TBMO_V2.html](https://daac.ornl.gov/NACP/guides/NACP_MsTMIP_TBMO_V2.html)]([https://daac.ornl.gov/NACP/guides/NACP_MsTMIP_TBMO_V2.html](https://daac.ornl.gov/NACP/guides/NACP_MsTMIP_TBMO_V2.html)), accessed on 23 January 2024), MsTMIP provides monthly data values at a spatial resolution of 0.5\({}^{\circ}\) and a temporal resolution spanning from 1901 to 2010. Based on previous theoretical studies [14], four terrestrial  
Figure 1: Steps for processing raw land-use data into 0.05\({}^{\circ}\) and 0.5\({}^{\circ}\) grids. Figure 2: Masking results at different resolutions for China’s terrestrial ecosystems. (**a**) is the map with a resolution of 0.05\({}^{\circ}\); (**b**) is the map with a resolution of 0.5\({}^{\circ}\). ecosystem models were selected for inclusion in the MsTIMP multi-scenario model for this study: Community Land Model version 4 (CLM4) [15], CLM4-Variable Infiltration Capacity model (CLM4-VIC) [16], Dynamic Land Ecosystem Model (DMEM) [17], and Integrated Science Assessment Model (ISAM) [18]. Despite the standardized protocol used to derive initial conditions, different models show a high degree of variation for GPP, so we applied an ensemble average of the four GPP model data to reduce the uncertainty of the GPP data. The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) also is a model comparison plan. The objective is to establish a framework for comparing risk models globally and regionally across various sectors, facilitating coordinated multi-sectoral assessments of diverse risks and their cumulative impacts to support decision-making on adaptation and mitigation at both the global and regional levels [19]. This study selected four sets of monthly GPP data, each with a spatial resolution of 0.5\({}^{\circ}\) and a temporal resolution spanning from 1901 to 2005, adhering strictly to the ISIMIP2b standard protocol. The meteorological forcing data utilized are from IPSL-CM5A-IR: CARbon Assimilation In the Biosphere (CARAIB) [20], Lund-Potsdam-Jena: General Ecosystem Simulator (LPJ-GUESS) [21], Lund-Potsdam-Jena managed Land (LPJmL) [22], and Organizing Carbon and Hydrology in Dynamic EcosystEms: Dynamic Global Vegetation Model (ORCHIDE-DGVM) [23]. Similar to the handling of MsTMIP data, we applied an ensemble average of the four GPP model data to reduce uncertainty of the GPP data. Global Land Surface Satellite (GLASS) products, derived from multi-source remote sensing data and ground-truthing data, offer long-term, high-precision global surface remote sensing data. They serve as a dependable foundation for investigating global environmental change, facilitating the global, intercontinental, and regional monitoring of atmospheric dynamics, vegetation cover, water bodies, and more. These products, when integrated with climate change parameters like temperature and precipitation, enable the analysis of global change [24]. GPP, one of GLASS's key products, spans a lengthy period (1982-2018) with high temporal and spatial resolution (0.05\({}^{\circ}\), 8 days), making it optimal for analyzing temporal and spatial variations in GPP. The GLASS GPP algorithm is based on the Eddy Covariance-Light Use Efficiency (EC-LUE) model, which originally relies on four variables: remotely sensed NDVI, PAR, air temperature, and Bowen's ratio [25].