References：
[1] Fang, J. Y. Ecoclimatological analysis of the forest zones in China [J]. Acta Ecologica Sinica, 1991, 11(4): 377-387.
[2] Zhang, X S. A vegetation-climate classification system for global change studies in China [J]. Quaternary Sciences, 1993, 13(2): 157-169, 193.
[3] Ni, J. An introduction to bioclimatic factors in global change research [J]. Quaternary Sciences, 2017, 37(3): 431-441.
[4] Liu, X. T., Yuan, Q., Ni, J. Research advances in modelling plant species distribution in China [J]. Chinese Journal of Plant Ecology, 2019, 43(4): 273-283.
[5] Feng, J. M. Spatial patterns of species diversity of seed plants in China and their climatic explanation [J]. Biodiversity Science, 2008, 16(5): 470-476.
[6] Piao, S. L., Fang, J. Y., He, J. S., et al. Spatial distribution of grassland biomass in China [J]. Chinese Journal of Plant Ecology, 2004, 28(4): 491-498.
[7] Piao, S. L., Fang, J. Y., Zhou, L. M., et al. Changes in vegetation net primary productivity from 1982 to 1999 in China [J]. Global Biogeochemical Cycles, 2005, 19(2).
[8] Liu, L., Yang, H., Xu, Y., et al. Forest biomass and net primary productivity in southwestern China: a meta-analysis focusing on environmental driving factors [J]. Forests, 2016, 7(12): 173.
[9] Cui, S. P., Luo, X., Li, C. W., et al. Predicting the potential distribution of white-lipped deer using the MaxEnt model [J]. Biodiversity Science, 2018, 26(2): 171-176.
[10] Zhang, X. J., Gao, X. M., Ji, C. J., et al. Response of abundance distribution of five species of Quercus to climate change in Northern China [J]. Chinese Journal of Plant Ecology, 2019, 43(9): 774-782.
[11] Ni, J., Sykes, M. T., Prentice, I. C., et al. Modelling the vegetation of China using the process-based equilibrium terrestrial biosphere model BIOME3 [J]. Global Ecology and Biogeography, 2000, 9(6): 463-479.
[12] Ni, J. Carbon storage in terrestrial ecosystems of China: estimates at different spatial resolutions and their responses to climate change [J]. Climatic Change, 2001, 49(3): 339-358.
[13] Piao, S., Ciais, P., Huang, Y., et al. The impacts of climate change on water resources and agriculture in China [J]. Nature, 467(7311): 43-51.
[14] Yan, H. Modeling spatial distribution of climate in China using thin plate smoothing spline interpolation [J]. Scientia Geographica Sinica, 2004, 24(2): 163-169.
[15] Hijmans. R. J., Cameron, S. E., Parra, J. L., et al. Very high resolution interpolated climate surfaces for global land areas [J]. International Journal of Climatology, 2005, 25(15): 1965-1978.
[16] Tan, J. B., Li, A. N., Lei, G. B. Contrast on anusplin and cokriging meteorological spatial interpolation in southeastern margin of Qinghai-Xizang plateau [J]. Plateau Meteorology, 2016, 35(4): 875-886.
[17] Hancock, P. A., Hutchinson, M. F. Spatial interpolation of large climate data sets using bivariate thin plate smoothing splines [J]. Environmental Modelling & Software, 2006, 21(12): 1684-1694.
[18] Xu, T. B., Hutchinson, M. F. New developments and applications in the ANUCLIM spatial climatic and bioclimatic modelling package [J]. Environmental Modelling & Software, 2013, 40: 267-279.
[19] Fick, S. E., Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas [J]. International Journal of Climatology, 2017, 37(12): 4302-4315.
[20] Harris, I., Osborn, T. J., Jones, P., et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset [J]. Scientific Data, 2020, 7: 109.
[21] Chen, L. X., Zhou, X. J., Li, W. L., et al. Characteristics of the climate change and its formation mechanism in China in last 80 years [J]. Acta Meteorologica Sinica, 2004, 62(5): 634-646.
[22] Wang, J. B., Wang, J. W., Ye, H., et al. An interpolated temperature and precipitation dataset at 1 km grid resolution in China (2000-2012) [J/OL]. China Scientific Data, 2017, 2(1).
[23] Peng, S. Z., Ding, Y. X., Liu, W. Z., et al. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017 [J]. Earth System Science Data, 2019, 11(4): 1931-1946.
[24] Chen, W., Sun, L. Q., Li, Q. L., et al. An interpolation dataset for temperature and precipitation at 1km grid resolution in Chinese mainland for recent 38 Years [J]. Meteorological Science and Technology, 2021, 49(3): 355-361.
[25] Niu, Z. G., He, H. L., Zhu, G. F., et al. A spatial-temporal continuous dataset of the transpiration to evapotranspiration ratio in China from 1981-2015 [J]. Scientific Data, 2020(7): 369.
[26] He, J., Yang, K., Tang, W., et al. The first high-resolution meteorological forcing dataset for land process studies over China [J]. Scientific Data, 2020(7): 25.
[27] The Central Meteorological Administration Information Office. The Dataset for Climate of China’s Mainland [M]. Beijing: Meteorological Press, 1984.
[28] Hutchinson, M. F., Xu, T. B. ANUSPLIN Version 4.4 User Guide [M]. Canberra: Fenner School of Environment and Society, the Australian National University, 2013.
[29] Farr, T. G., Rosen, P. A., Caro, E., et al. The shuttle radar topography mission [J]. Reviews of Geophysics, 2007, 45(2): RG2004.
[30] Liu, Z. H., Mcvicar, T., Niel, V., et al. Introduction of the professional interpolation software for meteorology data: ANUSPLINN [J]. Meteorological Monthly, 2008, 34(2): 92-100.]
[31] Jiang, X. J., Liu, X. J., Huang, F., et al. Comparison of spatial interpolation methods for daily meteorological elements [J]. Chinese Journal of Applied Ecology, 2010, 21(3): 624-630.
[32] The Editorial Committee of Vegetation Map of China, Chinese Academy of Science. Vegetation Atlas of China (1:1000 000) [M]. Beijing: Science Press, 2001.
[33] Wei, L. F., Hu, X. F., Cheng Q., et al. A dataset of spatial distribution of bioclimatic variables in China at 1km resolution [J/OL]. China Scientific Data, 2022.
[34] Wu, X. Q., Cheng Q., Wei, L. F., et al. A time series dataset of climate variables from 1951 to 2014 in karst region of southwestern China [J/OL]. China Scientific Data, 2022.