高时空分辨率的精确长期气温和降水估算对于各种气候学研究至关重要。我们制作了一个新的、可公开获取的中国日网格最高气温、最低气温和降水量数据集，其空间分辨率高达 1 千米，涵盖了一个长期时期（1961 年至 2019 年）。该数据集被命名为 HRLT,基于中国气象局的 0.5°× 0.5°网格数据集，以及海拔、高差、坡度、地形湿润指数、纬度和经度等协变量。利用中国各地气象站的观测数据对 HRLT 日数据集的准确性进行了评估。最高气温和最低气温的估算比降水量的估算更为准确。最高气温的平均绝对误差 (MAE)、均方根误差 (RMSE)、皮尔逊相关系数 (Cor)、调整后决定系数 (R2) 和 Nash-Sutcliffe 建模效率 (NSE) 分别为 1.07°C、1.62 °C、0.99、0.98 和 0.98。最低气温的 MAE、RMSE、Cor、R2和 NSE 分别为 1.08°C、1.53°C、0.99、0.99 和 0.99。降水的 MAE、RMSE、Cor、R2和 NSE 分别为 1.30 mm、4.78 mm、0.84、0.71 和 0.70。将 HRLT 的精度与其他三个现有数据集的精度进行了比较，结果表明，HRLT 的精度高于其他数据集，尤其是降水数据集；或者，HRLT 的精度与其他数据集相当，但空间分辨率更高，时间跨度更长。总之，HRLT 数据集空间分辨率高，覆盖时间更长，精度可靠。
", "ds_source": "从中国气象数据服务中心获取了中国日地表温度 0.5°×0.5°网格数据集和中国日降水量 0.5°×0.5°网格数据集（V2.0）（https://data.cma.cn/，最后访问日期：2022 年 9 月 15 日）作为基本输入数据。研究人员还报告了来自 CAM 数据集的 1961-2010 年日降水量 0.5°× 0.5°网格数据集。基本地形数据，包括高程、流向和流量来自HydroSHEDS数据库。
\n", "ds_process_way": "在本研究中，利用综合统计分析对日网格数据进行了插值，包括机器学习方法、广义加法模型和薄板样条。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "1961-01-01 00:00:00", "ds_acq_end_time": "2019-12-31 00:00:00", "ds_acq_place": "中国", "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": 891892034870, "ds_files_count": 178, "ds_format": ".nc", "ds_space_res": "1000", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "07bf1dde-c0f7-4a7c-ba2d-648843b8a8fc.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "99c0a56f-14cb-4cfc-a9a1-bb4b8d16a658", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2023-11-27 16:05:21", "last_updated": "2025-05-29 11:22:02", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.PANGAEA.DB4100.2023", "i18n": { "en": { "title": "HRLT: A high-resolution (1 day, 1 km) and long-term gridded dataset for temperature and precipitation across China(1961–2019)", "ds_format": ".nc", "ds_source": "The CMA dataset, which includes the daily surface temperature 0.5∘ × 0.5∘ gridded dataset and the daily precipitation 0.5∘ × 0.5∘ gridded dataset for China (V2.0) (https://data.cma.cn/, last access: 15 September 2022), was obtained from the China Meteorological Data Service Centre and was used as the basic input data. The researchers also reported a daily precipitation 0.5∘ × 0.5∘ gridded dataset for 1961–2010 from the CAM dataset.Basic topographic data, including elevation, flow direction, and flow rate were obtained from the HydroSHEDS database.
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "Accurate long-term temperature and precipitation estimates at high spatial and temporal resolutions are vital for a wide variety of climatological studies. We have produced a new, publicly available, daily, gridded maximum temperature, minimum temperature, and precipitation dataset for China with a high spatial resolution of 1 km that covers a long-term period (1961 to 2019). It has been named the HRLT,It was based on the 0.5°× 0.5° gridded dataset from the China Meteorological Administration, together with covariates for elevation, aspect, slope, topographic wetness index, latitude, and longitude. The accuracy of the HRLT daily dataset was assessed using observation data from meteorological stations across China. The maximum and minimum temperature estimates were more accurate than the precipitation estimates. For maximum temperature, the mean absolute error (MAE), root mean square error (RMSE), Pearson's correlation coefficient (Cor), coefficient of determination after adjustment (R2), and Nash–Sutcliffe modeling efficiency (NSE) were 1.07°C, 1.62°C, 0.99, 0.98, and 0.98, respectively. For minimum temperature, the MAE, RMSE, Cor, R2, and NSE were 1.08°C, 1.53°C, 0.99, 0.99, and 0.99, respectively. For precipitation, the MAE, RMSE, Cor, R2, and NSE were 1.30 mm, 4.78 mm, 0.84, 0.71, and 0.70, respectively. The accuracy of the HRLT was compared to those of three other existing datasets, and its accuracy was either greater than the others, especially for precipitation, or comparable in accuracy, but with higher spatial resolution or over a longer time period. In summary, the HRLT dataset, which has a high spatial resolution, covers a longer period of time and has reliable accuracy.
", "ds_time_res": "年", "ds_acq_place": "China", "ds_space_res": "1000", "ds_projection": "", "ds_process_way": "In this study, the daily gridded data were interpolated using comprehensive statistical analyses, which included machine learning methods, the generalized additive model, and thin plate splines.
", "ds_ref_instruction": "When using data, please clearly state the source of the data in the main text and cite the citation provided by this metadata in the reference section." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "中国", "降水", "温度", "高时空分辨率", "长期" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 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 ], "ds_contributors": [ { "true_name": "张峰", "email": "zhangfeng@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张峰", "email": "zhangfeng@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_managers": [ { "true_name": "张峰", "email": "zhangfeng@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "category": "水文" }