A 1 km monthly dataset of historical and future climate changes over China

This dataset provides 30-year averaged climate data for both historical and future periods, with a spatial resolution of 0.01° × 0.01°. Historical data (1991–2020) are based on the China Surface Climate Standard Dataset and were interpolated using ANUSPLIN software. Future climate data are derived from CMIP6 simulations, bias-corrected using the Delta downscaling method. The dataset includes 10 models (9 Global Climate Models, namely, GCMs, and 1 ensemble model), 3 scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), and 3 future periods (2021–2040, 2041–2070, 2071–2100). For each period (or scenario), 28 climate variables are provided, including: 5 monthly basic climate variables (mean temperature, maximum temperature, minimum temperature, precipitation, and percentage of sunshine), and 23 bioclimatic variables based on the basic variables (for details, see the dataset documentation file).The data quality was strictly evaluated. The ANUSPLIN interpolated historical data showed a strong correlation with observations (all correlation coefficients above 0.91). The historical interpolations generated by the ANUSPLIIN software showed a good fit (above 0.91) with observations. The bias correction improved the accuracy of most GCM original simulations, reducing the bias by 0.69%–58.63%. This dataset aims to provide high-resolution, bias-corrected long-term historical and future climate data for climate and ecological research. All computations were performed using R, and the corresponding code can be found in the dataset folder: “Code”.All data are provided in GeoTIFF (.tif) format, where each file for the basic climate variables contains 12 bands, representing monthly data in ascending order (e.g., Band 1 corresponds to January). To facilitate data storage, all files are provided in compressed archives, following a consistent naming convention:(1) Historical data: China_Variable_1km_1991–2020.tifWhere, Variable represents the abbreviation of the 28 climate variables.Example: China_pr_1km_1991–2020.tif.(2) Future data: China_Variable_Model_VariantLabel_1km_StartYear-EndYear_Scenario.tifWhere, Variable is the 28 climate variables; Model is the GCM name; VariantLabel is r1i1p1f1 in this study; StartYear-EndYear is the future period; Scenario is the SSP climate scenarioExample: China_tasmin_MRI-ESM2-0_r1i1p1f1_1km_2071–2100_SSP585.tif.

本数据集提供历史与未来时段的30年平均气候数据，空间分辨率为0.01°×0.01°。历史时段数据（1991–2020年）基于《中国地面气候标准值数据集》构建，采用ANUSPLIN软件进行插值处理。未来气候数据源自CMIP6模拟结果，并通过Delta降尺度方法完成偏差校正。本数据集涵盖10个模式（9个全球气候模式（Global Climate Models，简称GCMs）与1个集合模式）、3种气候情景（SSP1-2.6、SSP2-4.5及SSP5-8.5）以及3个未来时段（2021–2040年、2041–2070年、2071–2100年）。针对每个时段或情景，数据集提供28种气候变量，其中包含5种月度基础气候变量：平均气温、最高气温、最低气温、降水量与日照百分率，以及基于基础变量衍生的23种生物气候变量（详细信息请参阅数据集说明文档）。本数据集已完成严格的质量验证：经ANUSPLIN软件插值得到的历史气候数据与实测值相关性极强，所有相关系数均高于0.91；插值结果与观测值拟合度优异（拟合优度均大于0.91）。偏差校正步骤有效提升了多数GCM原始模拟结果的精度，将偏差降低了0.69%至58.63%。本数据集旨在为气候与生态研究提供高分辨率、经偏差校正的长时序历史与未来气候数据。所有计算均通过R语言完成，对应的代码可在数据集文件夹的"Code"目录中获取。所有数据均采用GeoTIFF（.tif）格式存储，其中基础气候变量的单个文件包含12个波段，按升序对应各月度数据（例如，波段1代表1月数据）。为便于数据存储，所有文件均以压缩归档形式提供，并遵循统一的命名规范：1. 历史数据：China_Variable_1km_1991–2020.tif，其中Variable为28种气候变量的缩写，示例：China_pr_1km_1991–2020.tif。2. 未来数据：China_Variable_Model_VariantLabel_1km_StartYear-EndYear_Scenario.tif，其中Variable为28种气候变量的缩写，Model为GCM名称，VariantLabel在本研究中为r1i1p1f1，StartYear-EndYear为未来时段，Scenario为SSP气候情景，示例：China_tasmin_MRI-ESM2-0_r1i1p1f1_1km_2071–2100_SSP585.tif。

搜集汇总

背景与挑战

背景概述

这是一个高分辨率（约1公里）的中国历史和未来气候变化月度数据集，提供1991–2020年历史时期和三个未来时期（2021–2040、2041–2070、2071–2100）的30年平均气候数据。数据集基于中国地面气候标准数据集和CMIP6模拟，经过ANUSPLIN插值和Delta方法偏差校正，确保数据准确性；包括10个模型、3个SSP情景和28个气候变量，以GeoTIFF格式存储，专为气候和生态研究设计。