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Based on the observation monthly climatic data collected from 2766 weather observation stations on global during the period from 1981 to 2010, and the climatic scenarios data of SSP1_2.6、SSP1_4.5 and SSP1_8.5 scenarios released by CMIP6, the mean annual biotemperature, average total annual precipitation and potential evapotranspiration ratio on spatial resolution of 0.1º× 0.1º were respectively obtained by operating a high accuracy and speed method of surfacing modeling (HASM) (Yue, 2010, Yue et al., 2016) during all the four periods from 2020 to 2050 per decade. The method for surface modelling of land cover scenarios (SMLCS) has been developed to simulate the scenarios of land cover in Eurasia （Fan et al., 2019, 2020, 2021）. Finally, the scenario dataset of land cover under scenario SSP1_2.6、SSP1_4.5 and SSP1_8.5 were simulated by the SMLCS method from 2020 to 2050. 采用1981-2010年全球2766个气象观测站的观测月气候数据，以及CMIP6发布的SSP1_2.6、SSP1_4.5和SSP1_8.5情景的气候情景数据。通过运行高精度面建模方法（HASM）(Yue, 2010, Yue et al., 2016)，分别获得2020-2050年间每10年的空间分辨率为0.1º×0.1º的平均生物温度数据、多年平均年降水和潜在蒸散比率数据。采用自主研发的土地覆被情景曲面建模（SMLCS）方法（Fan et al., 2019, 2020, 2021），实现了SSP1_2.6、SSP1_4.5和SSP1_8.5情景的2020-2050年间每10年的全球土地覆被变化情景模拟。

Spatio-temporal variations in extreme drought in China during 1961–2015 Spatio-temporal variations in extreme drought in China during 1961–2015

Vegetation phenology is one of the sensitive indicators reflecting global climate change and vegetation growth. Inner Mongolia is an important ecological security barrier in the north of China, and a key area for resource development, environmental protection and ecological security in China. Studying its vegetation phenological changes can know its vegetation growth status, which is of great significance for understanding the characteristics of climate change and extreme climate events in the region. Based on the normalized differential vegetation index (NDVI) data product in MOD13Q1 product, this study use Google Earth Engine platform to process MODIS-NDVI raw data for format conversion, projection conversion and clipping, and exports NDVI long time series data from 2000 to 2021, and dynamic threshold method was used to obtain Inner Mongolia vegetation phenology data set from 2001 to 2020. The dataset includes remote sensing monitoring data of the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS) in Inner Mongolia from 2001 to 2019. And the spatial resolution is 250 m. It provides data support for understanding the temporal and spatial variation of vegetation phenology in Inner Mongolia and its response to climate change. Vegetation phenology is one of the sensitive indicators reflecting global climate change and vegetation growth. Inner Mongolia is an important ecological security barrier in the north of China, and a key area for resource development, environmental protection and ecological security in China. Studying its vegetation phenological changes can know its vegetation growth status, which is of great significance for understanding the characteristics of climate change and extreme climate events in the region. Based on the normalized differential vegetation index (NDVI) data product in MOD13Q1 product, this study use Google Earth Engine platform to process MODIS-NDVI raw data for format conversion, projection conversion and clipping, and exports NDVI long time series data from 2000 to 2021, and dynamic threshold method was used to obtain Inner Mongolia vegetation phenology data set from 2001 to 2020. The dataset includes remote sensing monitoring data of the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS) in Inner Mongolia from 2001 to 2019. And the spatial resolution is 250 m. It provides data support for understanding the temporal and spatial variation of vegetation phenology in Inner Mongolia and its response to climate change.

Based on the observation monthly climatic data collected from 2127 weather observation stations in Eurasia during the period from 1981 to 2010, and the climatic scenarios data of RCP2.6, RCP4.5 and RCP8.5 scenarios released by CMIP5, the mean annual biotemperature, average total annual precipitation and potential evapotranspiration ratio on spatial resolution of 0.125º× 0.125º were respectively obtained by operating a high accuracy and speed method of surfacing modeling (HASM) (Yue, 2010, Yue et al., 2016) during all the four periods from 1981 to 2010(T0), 2011 to 2040(T1), 2041 to 2070(T2), and 2071 to 2100(T3). The method for surface modelling of land cover scenarios (SMLCS) has been developed to simulate the scenarios of land cover in Eurasia (Fan et al., 2019). Finally, the scenario dataset of land cover under scenario RCP2.6, RCP4.5 and RCP8.5 were simulated by the SMLCS method from 2010 to 2100. 采用1981-2010年欧亚大陆2127个气象观测站的观测月气候数据，以及CMIP5发布的RCP2.6、RCP4.5和RCP8.5情景的气候情景数据。通过运行高精度面建模方法（HASM）(Yue, 2010, Yue et al., 2016)，分别获得1981-2010年（T0）、2011-2040年（T1）、2041-2070年（T2）和2071-2100年（T3）四个时期的空间分辨率为0.125º×0.125º的平均生物温度数据、多年平均年降水和潜在蒸散比率数据。采用自主研发的土地覆被情景曲面建模（SMLCS）方法，以模拟欧亚大陆的土地覆盖场景(Fan et al., 2019)）。最后，利用SMLCS方法对2010-2100年场景RCP2.6、RCP4.5和RCP8.5下的土地覆盖场景数据集进行了模拟。