This study used 21 climate variables, including 19 bioclimatic variables, solar radiation (RAD), and drought index (AI). 19 bioclimatic variables and solar radiation data from WorldClim version 2.1 from 1970 to 2000（ https://www.worldclim.org/data/worldclim21.html ）.

    The artificial impact data is sourced from the Consultative Group on International Agricultural Research (CGIAR-CSI) Spatial Information Consortium from 1970 to 2000 http://www.csi.cgiar.org The spatial resolution of climate data is 1 kilometer.

    This study included 12 soil variables representing different aspects of soil properties, namely soil texture, bulk density (BD), pH value, and soil nutrients. All soil variables were extracted from the Chinese soil database for surface modeling http://globalchange.bnu.edu.cn/research/soil2

    The terrain variable is elevation. Elevation data based on SRTM V4.1 database（ https://www.resdc.cn/data.aspx?DATAID=123 Extract from the Chinese STRM 90m dataset. The spatial resolution is 1 km.

    This study includes three types of vegetation indices. Firstly, EVI is extracted from MOD13A3 V006 product（ https://lpdaac.usgs.gov/products/mod13a3v006/ ）. This product provides monthly averages with a spatial resolution of 1 kilometer, ranging from January 2000 to December 2018. Secondly, MODIS reflectance data was extracted from MOD13A3 V006 product, including MIR reflectance, NIR reflectance, red reflectance, and blue reflectance. Thirdly, the MERIS Land Chlorophyll Index (MTCI) was obtained from the Earth Observation Data Center of the Natural Environment Research Council (NEODC, 2005)（ https://data.ceda.ac.uk/ ）Extracted from within. From June 2002 to December 2011, MTCI data was provided globally with a spatial resolution and range of 4.63 kilometers. It should be noted that the effective MTCI value should be greater than 1, so our study removed any values less than 1.

    1. Field measurement data of six plant functional traits were collected from TRY and Chinese databases, as well as publicly published literature, and the functional traits of plant species were classified based on plant growth morphology, leaf type, and leaf phenology. After avoiding collinearity between climate, soil, terrain, and vegetation indices, multiple grid prediction factors were used.

    2. Use random forests and boosting regression trees to train the relationship between plant functional traits and predictive factors for each PFT.

    3. Use a land cover map (100 meters) to calculate the spatial abundance of each PFT within a 1-kilometer grid cell. Calculate the community weighted trait values within a 1-kilometer grid cell based on the abundance and predicted trait values of each PFT in step 2. In order to reduce the variability of different single models, we used the set model algorithm. According to the cross validation R2 value of random forest trees and lifting regression trees, we combined their prediction results to obtain the final spatial distribution map of plant functional traits.

    This study was compared with research worldwide, and therefore, data from China was extracted from the global trait map. Before quantitatively comparing with previous studies, we carried out two steps to make the data products as comparable as possible and improve consistency between different studies. Firstly, due to the spatial resolution of the global trait map (mainly 0.5 °) being different from our study, we resampled the data products from previous research and our map to a spatial resolution of 0.5 °. In addition to Vallicrosa et al. (2022) generating global maps of LNC and LPC at a spatial resolution of 1 kilometer, we also compared the frequency distribution of Vallicrosa et al. (2022) with our study at a spatial resolution of 1 kilometer. Secondly, our research focuses on natural vegetation, so we filtered out non natural vegetation (such as cultivated land) using a global trait map. We quantitatively compared our map with previous research from two aspects. The comparison between trait maps uses frequency charts and spatial correlations.