2021-2100年中国1km分辨率多情景多模式逐月平均气温数据集

数据集为中国多情景多模式逐月平均气温数据，空间分辨率为0.0083333°(约1km),时间为2021年1月-2100年12月。数据为.nc格式。数据是根据IPCC耦合模式比较计划第六阶段（CMIP6）发布的全球>100 km气候模式数据集以及WorldClim发布的全球高分辨率气候数据集，通过Delta空间降尺度方案在中国地区降尺度生成。数据采用IPCC最新发布的SSP情景(SSP119、SSP245、SSP585),每个情景包含三个GCMs（EC-Earth3、GFDL-ESM4、MRI-ESM2-0）气候数据，数据集包含的地理空间范围是中国主要陆地，不含南海岛礁等区域。单位为0.1℃。文件命名是GCM_SSP_tmp-30s-序号.nc，30s即0.0083333°，序号从1-40，序号1表示2021.1-2022.12，依次表示年份；以EC-Earth3_ssp119_tmp-30s-1.nc文件为例，表示SSP119情景下EC-Earth3气候模的1km分辨率2021.1-2022.12逐月均温数据，含24个图层。欲更深入的理解数据请参阅文献引用方式下的数据作者已发表的论文。

This dataset consists of monthly average air temperature data under multiple scenarios and multiple models across mainland China, with a spatial resolution of 0.0083333° (approximately 1 km), covering the period from January 2021 to December 2100. The data is stored in .nc format.

It was downscaled over mainland China using the Delta spatial downscaling scheme, based on two source datasets: the global climate model datasets with a resolution greater than 100 km released under the IPCC Coupled Model Intercomparison Project Phase 6 (CMIP6), and the global high-resolution climate datasets published by WorldClim.

This dataset adopts the latest IPCC SSP scenarios (SSP119, SSP245, SSP585), and each scenario includes climate datasets from three GCMs: EC-Earth3, GFDL-ESM4, and MRI-ESM2-0.

The geographical coverage is the main land areas of China, excluding regions such as the South China Sea islands and reefs. All temperature values are reported in units of 0.1℃.

The file naming convention follows the format: GCM_SSP_tmp-30s-serial_number.nc, where "30s" refers to 0.0083333°, and the serial number ranges from 1 to 40. Serial number 1 represents the period from January 2021 to December 2022, and the remaining serial numbers correspond to the sequential annual periods in turn. Taking the file "EC-Earth3_ssp119_tmp-30s-1.nc" as an example, it denotes the 1 km resolution monthly average air temperature data for the period January 2021 to December 2022 using the EC-Earth3 climate model under the SSP119 scenario, which contains 24 layers.

For further insights into this dataset, please refer to the published papers of the data authors as cited in the relevant literature.