气温时序数据是全球和区域气候研究、水文模拟及生态系统评估中的关键基础数据。然而，现有市县气温数据序列普遍较短。这导致当前对区域尺度，尤其是市县等局部单元上的长期气温动态与趋势变化的评估仍存在不确定性。本研究基于ACCESS-ESM1-5、IPSL-CM6A-LR、MIROC-ES2L等全球气候模式的未来情景数据，生成黄土高原不同市县（如延安市、兰州市、天水市等）2015-2100年未来格点气温数据产品。本数据集的核心优势在于本数据集的核心优势在于提供了2015-2100年共86年的长序列气温数据。这一完整的长时间序列有助于揭示局地气候对全球变化的响应细节，从而为市县尺度的长期气候风险评估、适应性规划提供坚实的数据基础。
\n本数据以Excel格式（*.xlsx）存储。数据文件中不同工作表为不同市县未来气温数据，ACCESS-ESM1-5、IPSL-CM6A-LR、MIROC-ES2L为不同模型，ssp126、ssp245、ssp585为排放情景，时间分辨率为年，时间范围为2015-2100年。", "ds_source": "
高分辨率观测数据采用基于欧洲中期天气预报中心第五代再分析数据（ERA5）开发的MSWX格点化偏差校正气象数据集，该数据集提供了10个近地表气象变量，空间分辨率为0.1°，时间分辨率为3小时，作为模型训练和验证的对照组（https://www.gloh2o.org/mswx/）。基础气候数据来自耦合模式比较计划第六阶段（CMIP6）中3个全球气候模式的输出，包括历史时期（1950-2014年）和三种未来情景（SSP1-2.6, SSP2-4.5, SSP5-8.5，2015-2100年）的逐日模拟，其空间分辨率介于0.7°至2.8°之间（https://esgf-node.llnl.gov/search/cmip6/）
", "ds_process_way": "（1）利用MSWX数据集作为高分辨率观测数据，用于后续偏差校正与模型训练；（2）采用分位数Delta映射方法对CMIP6的3个全球气候模式输出进行偏差校正，校正后的数据用于降尺度输入；（3）基于U-Net深度学习架构构建降尺度模型，针对不同季节分别训练四个独立的U-Net模型，输入包括地形数据与偏差校正后的气象变量，并采用改进的损失函数以提升极端事件模拟能力；（4）利用1982–2014年期间的数据进行模型训练与验证，通过对比MSWX观测数据，从气候均值、极端事件频率及未来变化等多个方面系统评估降尺度数据集的性能。（5）基于经验证可靠的降尺度格点数据集，利用GIS空间分析技术依据标准的市县级行政区矢量边界，计算每个行政单元内所有格点的年气温平均值，生成黄土高原不同市县气温的逐年序列。
", "ds_quality": "本研究采用均方根误差（RMSE）与平均偏差（MB）作为核心验证指标，系统评估了降尺度生成的日最高气温（tasmax）及日最低气温（tasmin）数据与MSWX高分辨率观测数据在1979年至2014年整个历史时期的一致性。
\n验证结果表明，降尺度后气温数据的精度得到根本性提升。具体而言，多模式集合平均的日最高气温（tasmax）RMSE从原始CMIP6输出的2.31°C以上普遍降至0.58°C以下，其MB范围也由-1.27至1.13°C改善至-0.52至-0.27°C；日最低气温（tasmin）的RMSE则由最优原始模式的2.32°C降至0.78°C。此外，数据能近乎完美地再现气温的季节循环，其多模式集合平均与观测的相关系数达1.0，且模型间不确定性显著降低。该降尺度气温数据在修正当前偏差的同时，也保留了原始模式预估的未来增温趋势，例如在SSP5-8.5情景下，其21世纪末的预估增温幅度（4.52°C）与原始模式（4.58°C）高度一致。因此，该降尺度气温数据集能够更可靠地反映区域气温的实际情况，可作为黄土高原不同市县气候研究的可靠数据基础。
", "ds_acq_start_time": "2015-01-01 00:00:00", "ds_acq_end_time": "2100-12-31 00:00:00", "ds_acq_place": "黄土高原", "ds_acq_lon_east": 114.53333333333333, "ds_acq_lat_south": 33.75, "ds_acq_lon_west": 100.95, "ds_acq_lat_north": 41.25, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 219099, "ds_files_count": 0, "ds_format": "*.xlsx", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "05a4b51d-9d2e-485f-8ca6-6a7da9877ab6.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": "", "organization_id": "bf138922-7121-438c-8d1b-19d5f751c907", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.15", "170.4510" ], "quality_level": 0, "publish_time": "2026-06-04 11:37:30", "last_updated": "2026-06-04 11:37:30", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.loess.db7335.2026", "i18n": { "en": { "title": "Annual Temperature Dataset for Different Cities and Counties on the Loess Plateau (2015–2100)", "ds_format": "*.xlsx", "ds_source": "The high-resolution observational data used in this study are based on the MSWX gridded bias-corrected meteorological dataset developed from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5). This dataset provides 10 near-surface meteorological variables at a spatial resolution of 0.1° and a temporal resolution of 3 hours, serving as the \"ground truth\" for model training and validation (https://www.gloh2o.org/mswx/). The baseline climate data were derived from outputs of three global climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), including historical simulations (1950–2014) and daily projections under three future scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5, 2015–2100). The spatial resolution of these climate models ranges from 0.7° to 2.8° (https://esgf-node.llnl.gov/search/cmip6/).", "ds_quality": "
The validation results demonstrate a fundamental improvement in the accuracy of the downscaled temperature data. Specifically, the multi-model ensemble mean RMSE for daily maximum temperature (tasmax) was generally reduced from above 2.31 °C in the raw CMIP6 outputs to below 0.58 °C. Its MB range improved from -1.27 to 1.13 °C to a narrower range of -0.52 to -0.27 °C. The RMSE for daily minimum temperature (tasmin) decreased from 2.32 °C in the best-performing raw model to 0.78 °C. Furthermore, the data could nearly perfectly reproduce the seasonal cycle of temperature, with the multi-model ensemble mean showing a correlation coefficient of 1.0 against observations and a significant reduction in inter-model uncertainty. While correcting current biases, the downscaled temperature data also preserved the future warming trends projected by the original models. For example, under the SSP5-8.5 scenario, its projected warming magnitude by the end of the 21st century (4.52 °C) was highly consistent with that of the raw models (4.58 °C). Therefore, this downscaled temperature dataset can more reliably reflect actual regional temperature conditions and serves as a reliable data foundation for climate research in different cities and counties of the Loess Plateau.", "ds_ref_way": "", "ds_abstract": "
Temperature time series data are crucial fundamental data for global and regional climate research, hydrological modeling, and ecosystem assessments. However, existing temperature data series for cities and counties are generally short in duration. This creates uncertainty in assessing long-term temperature dynamics and trends at regional scales, especially for local administrative units such as cities and counties.This study utilizes future scenario data from global climate models, including ACCESS-ESM1-5, IPSL-CM6A-LR, and MIROC-ES2L, to generate a future gridded temperature data product for various cities and counties on the Loess Plateau (e.g., Yan'an City, Lanzhou City, Tianshui City) covering the period from 2015 to 2100.The core advantage of this dataset lies in its provision of a long-term, 86-year temperature data series from 2015 to 2100. This complete, extended temporal record facilitates the detailed analysis of local climatic responses to global change, thereby providing a solid data foundation for long-term climate risk assessment and adaptation planning at the city and county scale.\r\n
The dataset is stored in Excel format (*.xlsx). Different worksheets contain future air temperature data for different cities and counties. ACCESS-ESM1-5, IPSL-CM6A-LR and MIROC-ES2L are different climate models, and ssp126, ssp245, ssp585 represent different emission scenarios. The data adopts annual temporal resolution and covers the period from 2015 to 2100.", "ds_time_res": "", "ds_acq_place": "Loess Plateau", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
(1) The MSWX dataset was utilized as high-resolution observational data for subsequent bias correction and model training. (2) The Quantile Delta Mapping method was applied to perform bias correction on the outputs from three CMIP6 global climate models, with the corrected data then serving as input for downscaling. (3) A downscaling model was constructed based on the U-Net deep learning architecture. Four independent U-Net models were trained for different seasons, with inputs including terrain data and bias-corrected meteorological variables. A modified loss function was employed to enhance the simulation capability for extreme events. (4) Data from the period 1982–2014 were used for model training and validation. The performance of the downscaled dataset was systematically evaluated by comparing it with MSWX observational data across multiple aspects, including climatic means, extreme event frequency, and future changes. (5) Based on the validated and reliable downscaled gridded dataset, GIS spatial analysis techniques were applied in accordance with standard city/county-level administrative vector boundaries. The annual average temperature for all grid points within each administrative unit was calculated, generating annual time series of temperature for different cities and counties on the Loess Plateau.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "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, "belong_to_nieer": false, "ds_topic_tags": [ "气温", "长时序" ], "ds_subject_tags": [ "大气科学", "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "延安市", "兰州市", "天水市", "庆阳市", "西吉县", "泾阳县", "北吴起县", "绥德县", "衡山县" ], "ds_time_tags": [ 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 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": [ { "true_name": "张宝庆", "email": "baoqzhang@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张宝庆", "email": "baoqzhang@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_managers": [ { "true_name": "张宝庆", "email": "baoqzhang@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "category": "气象" }