降水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，尽管目前已存在多种降水数据产品，但由于黄土高原观测站分布不均、区域再分析数据与实测值之间存在的系统性偏差，当前对区域尺度长期降水动态与趋势变化的评估仍存在一定的不确定性。本研究利用中国气象驱动数据集（China Meteorological Forcing Dataset, CMFD）v1.7，生成黄土高原1982-2020年历史降水数据产品。该产品的空间范围为33.75°-41.25°N、100.95°-114.55°E，并有着0.1°的高空间分辨率与日时间分辨率。相较于其他数据集，本数据集的核心优势在于其能够更精细地刻画降水的时空分异规律，能够显著提升对局地极端降水事件的再现能力，从而为区域气候模拟、水文过程分析及极端降水事件研究提供更可靠的数据支持。
\n本数据以NetCDF格式（*.nc）存储。数据文件命名格式为LoessPlateau_prec_1982_2020。其中，LoessPlateau为地区名称，prec为变量名称。数据文件中yearly_day_prec为降水变量，时空分辨率分别为日、10 km，时间范围为1982-2020年，lat和long分别为格点的纬度和经度。", "ds_source": "
中国气象驱动数据集（China Meteorological Forcing Dataset, CMFD）v1.7是由中国科学院青藏高原研究所发布、专门针对中国陆面过程研究而开发的一套高时空分辨率格点气象数据集。该数据集通过融合中国气象局约700个地面观测站数据、多套卫星遥感产品以及国际再分析资料，生成了覆盖中国区域自1979年以来的长时间序列、时空连续的近地表气象要素驱动场。CMFD1.7提供了包括2米气温、地表气压、比湿、10米风速、向下短波与长波辐射以及降水率在内的7个关键变量，其空间分辨率高达0.1°（约10公里），时间分辨率为3小时，因其高质量和在中国区域的良好表现，已成为驱动各类陆面、水文及生态模型，以及进行区域气候分析的重要基准数据之一。
", "ds_process_way": "（1）采用覆盖全国范围的中国气象驱动数据集（CMFD）v1.7的3小时降水率格点数据作为基础输入数据源，从中提取并裁剪出黄土高原地区（北纬33.75°-41.25°，东经100.95°-114.55°）的时空数据子集；（2）将高时间分辨率的网格数据累加为日尺度数据，具体方法是将一天内8个3小时时次的降水数据（mm/hr）数据分别乘以时间步长（3小时）转换为时段降水量，再进行求和，最终生成每个格点的日总降水量（mm/day）；（3）通过对比区域内国家气象站观测的日降水数据，从平均偏差（MBE）、均方根误差（RMSE）和相关系数（R²）等多方面系统验证所生成的黄土高原日降水数据集的精度和可靠性。
", "ds_quality": "本研究采用平均偏差误差（MBE）、均方根误差（RMSE）与相关系数（R²）作为核心验证指标，系统评估了该降水数据与独立站点观测的一致性。验证结果表明，该降水数据在黄土高原地区与站点数据表现出高一致性。该数据与站点数据的平均偏差误差（MBE）为0.33 mm/day、均方根误差（RMSE）为0.98 mm/day、相关系数（R²）为0.97。该降水数据在误差大小及空间代表性上能够更可靠地刻画黄土高原地区降水的时空分布，适用于高分辨率陆面过程模拟及气候研究。
", "ds_acq_start_time": "1982-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "黄土高原", "ds_acq_lon_east": 114.55, "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": 594227370, "ds_files_count": 0, "ds_format": "*.nc", "ds_space_res": "0.1°", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "9c0c0d92-4fd0-4f95-bc74-9c7e024859c1.jpeg", "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" ], "quality_level": 0, "publish_time": "2026-05-21 09:10:50", "last_updated": "2026-05-21 09:10:50", "protected": false, "protected_to": null, "lang": "zh", "cstr": "", "i18n": { "en": { "title": "Long-term daily precipitation dataset for the Loess Plateau from 1982 to 2020", "ds_format": "*.nc", "ds_source": "The China Meteorological Forcing Dataset (CMFD) version 1.7 is a high spatiotemporal resolution gridded meteorological dataset specifically developed for land surface process studies over China by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences. By integrating ground-based observational data from approximately 700 stations of the China Meteorological Administration, multiple satellite remote sensing products, and international reanalysis datasets, it generates a long-term, spatiotemporally continuous near-surface meteorological forcing field covering China since 1979. CMFD version 1.7 provides seven key variables, including 2-meter air temperature, surface pressure, specific humidity, 10-meter wind speed, downward shortwave and longwave radiation, and precipitation rate. It features a high spatial resolution of 0.1 degrees (approximately 10 kilometers) and a temporal resolution of 3 hours. Due to its high quality and excellent performance over China, it has become a crucial benchmark dataset for driving various land surface, hydrological, and ecological models, as well as for conducting regional climate analysis.", "ds_quality": "
This study employed Mean Bias Error (MBE), Root Mean Square Error (RMSE), and the correlation coefficient (R²) as the core validation metrics to systematically evaluate the consistency between this precipitation dataset and independent station observations.\r\n
The validation results demonstrate a high level of consistency between this precipitation data and station observations in the Loess Plateau region. The dataset exhibits a Mean Bias Error (MBE) of 0.33 mm/day, a Root Mean Square Error (RMSE) of 0.98 mm/day, and a correlation coefficient (R²) of 0.97 when compared against the station data. With its reliable performance in terms of systematic bias, error magnitude, and spatial representativeness, this precipitation dataset can more accurately characterize the spatiotemporal distribution of precipitation in the Loess Plateau region. It is, therefore, well-suited for high-resolution land surface process modeling and climate research.", "ds_ref_way": "", "ds_abstract": "
Precipitation time series data are critical foundational data for global and regional climate research, hydrological modeling, and ecosystem assessments. However, despite the availability of various existing precipitation data products, the assessment of long-term precipitation dynamics and trend changes at the regional scale remains subject to a degree of uncertainty. This uncertainty stems primarily from the uneven distribution of observational stations across the Loess Plateau and systematic biases between reanalysis data and measured values. To address this, this study utilizes the China Meteorological Forcing Dataset (CMFD) version 1.7 to generate a historical precipitation data product for the Loess Plateau covering the period from 1982 to 2020. The spatial coverage of the dataset spans from 33.75°N to 41.25°N and 100.95°E to 114.55°E, with a high spatial resolution of 0.1° and a daily temporal resolution. Compared to other datasets, the core advantage of this product lies in its ability to provide high-resolution precipitation data that can more finely characterize the spatio-temporal distribution of precipitation. This significantly enhances the simulation of localized extreme precipitation events, thereby offering more reliable data support for regional climate modeling, hydrological process analysis, and research on extreme precipitation events.\r\n
This dataset is stored in NetCDF format (*.nc). The naming convention of data files is LoessPlateau_prec_1982_2020, in which LoessPlateau represents the study area and prec denotes the variable name.The precipitation variable in the files is named yearly_day_prec, with a temporal resolution of daily and a spatial resolution of 10 km, covering the period from 1982 to 2020. Variables lat and lon represent the latitude and longitude of grid points respectively.", "ds_time_res": "", "ds_acq_place": "Loess Plateau", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
(1) The 3-hourly precipitation rate gridded data from the nationwide China Meteorological Forcing Dataset (CMFD) version 1.7 was adopted as the foundational input data source. A spatiotemporal data subset for the Loess Plateau region (33.75°N–41.25°N, 100.95°E–114.55°E) was extracted and cropped from this source. (2) The high-temporal-resolution gridded data was aggregated to a daily scale. The specific method involved converting each of the eight 3-hourly precipitation data points (mm/hr) within a day into period-specific precipitation amounts by multiplying by the time step length (3 hours), followed by summation to ultimately generate the daily total precipitation (mm/day) for each grid point. (3) The accuracy and reliability of the generated daily precipitation dataset for the Loess Plateau were systematically validated from multiple aspects, including Mean Bias Error (MBE), Root Mean Square Error (RMSE), and the correlation coefficient (R²), by comparing it with observed daily precipitation data from national meteorological stations within the region.", "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, "ds_topic_tags": [ "降水", "长时序", "高分辨率", "高精度" ], "ds_subject_tags": [ "大气科学" ], "ds_class_tags": [], "ds_locus_tags": [ "黄土高原" ], "ds_time_tags": [ 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "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" }