GFDL-ESM4模式下CHELSA CMIP6 ISIMIP3数据，包括未来情景（2041-2070年、2071-2100年）的19个气候因子变量数据。选用共享社会经济路径（Shared Socio-Economic Pathways，简称SSP）下三种气候情景数据：SSP126，SSP370和SSP585。\nBIO1 = 年均气温，℃，缩放因子0.1，偏移量-273.15。\nBIO2 = 日均温差，℃，缩放因子0.1，偏移量0。\nBIO3 = 等温，日温差与年温差之比，℃，缩放因子0.1，偏移量0。\nBIO4 = 温度季节性，月平均温度的标准差，℃/100，缩放因子0.1，偏移量0。\nBIO5 = 最暖月的最高日均温，一年中最暖月内日最高温均值的最高温度，℃，缩放因子0.1，偏移量-273.15。\nBIO6 = 最冷月的最低日均温，一年中最冷月份的日均最低温的最低值，℃，缩放因子0.1，偏移量-273.15。\nBIO7 = 年均温差，最暖月最高温与最冷月最低温的差值，℃，缩放因子0.1，偏移量0。\nBIO8 = 最湿季度平均温度，一年中降水量最多的三个月（即最湿润季度）的平均温度，℃，缩放因子0.1，偏移量-273.15。\nBIO9 = 最干季度平均气温，一年中降水量最少的三个月（即最干燥季度）的平均气温，℃，缩放因子0.1，偏移量-273.15。\nBIO10 = 最暖季平均气温，℃，缩放因子0.1，偏移量-273.15。\nBIO11 = 最冷季平均气温，℃，缩放因子0.1，偏移量-273.15。\nBIO12 = 年降水量，mm/year，缩放因子0.1，偏移量0。\nBIO13 = 最湿月降水量，mm/month，缩放因子0.1，偏移量0。\nBIO14 = 最干月降水量，mm/month，缩放因子0.1，偏移量0。\nBIO15 = 降水季节性 (变异系数) ，缩放因子0.1，偏移量0\nBIO16 = 最湿季月均降水量，mm/month，缩放因子0.1，偏移量0。\nBIO17 = 最干季月均降水量，mm/month，缩放因子0.1，偏移量0。\nBIO18 = 最暖季月均降水量，mm/month，缩放因子0.1，偏移量0。\nBIO19 = 最冷季月均降水量，mm/month，缩放因子0.1，偏移量0。
", "ds_source": "数据来自https://chelsa-climate.org/，CHELSA (Climatologies at high resolution for the earth’s land surface areas，地球陆面高分辨率气候学) 数据集。该数据集包括降尺度模式输出的温度和降水，分辨率为30″。温度算法主要基于大气温度的统计降尺度。降水算法结合了包括风场、山谷地形特征和边界层高度在内的地貌预测因子，并随后进行了偏差校正。缩放和偏移存储在 GeoTIFF 文件中，GDAL 版本 2.3 或更高版本支持。
", "ds_process_way": "", "ds_quality": "", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 36740261518, "ds_files_count": 120, "ds_format": "Geotiff格式", "ds_space_res": "30″", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "WGS84", "ds_thumbnail": "98f279b4-d3d3-496c-8718-34b348791c94.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.15" ], "quality_level": 1, "publish_time": "2026-08-25 19:07:06", "first_publish_time": null, "last_updated": "2026-08-25 19:07:06", "protected": false, "protected_to": null, "lang": "zh", "cstr": null, "license": null, "extra": null, "files_shape": [ { "name": "2041-2070", "size": null, "is_dir": true }, { "name": "2071-2100", "size": null, "is_dir": true } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "", "ds_abstract": "CHELSA CMIP6 ISIMIIP 3 data under the GFDL-ESM4 model, including data on 19 climate factor variables for future scenarios (2041-2070, 2071-2100). Select three climate scenario data under the Shared Socio-Economic Pathways (SSP): SSP126, SSP370 and SSP585.", "ds_source": "Data from https://chelsa-climate.org/, CHELSA (Climatologies at high resolution for the earth's land surface areas) dataset. The dataset includes temperature and precipitation output from downscaled models with a resolution of 30 \". The temperature algorithm is mainly based on statistical downscaling of atmospheric temperature. The precipitation algorithm combines geomorphological prediction factors including wind fields, valley terrain characteristics and boundary layer heights, and then corrects for deviations. Scaling and offsets are stored in GeoTIFF files and are supported in GDAL version 2.3 or later.", "ds_process_way": "", "ds_quality": "", "ds_acq_place": "", "ds_ref_instruction": "", "ds_ref_way": "", "ds_format": "", "ds_projection": "", "ds_space_res": "", "ds_time_res": "" } }, "license_type": null, "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "生物气候变量", "年平均气温", "年降水量" ], "ds_subject_tags": [ "大气科学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100 ], "ds_contributors": [ "邵倩影" ], "ds_meta_authors": [ "邵倩影" ], "ds_managers": [ "赵金" ], "category": "遥感再分析数据" }