本数据集为英国东安格利亚大学气候研究中心（Climatic Research Unit，University of East Anglia）发布的 CRU TS 4.07 全球逐月气候格点数据，包含潜在蒸散发（pet）和降水（pre）两类变量，以 NetCDF（.nc）格式存储。数据空间范围覆盖全球陆地，空间分辨率为 0.5°×0.5°（约 55 km），时间分辨率为逐月。其中，潜在蒸散发数据（cru_ts4.07.1901.2022.pet.dat.nc）时间范围为 1901—2022 年；降水数据（cru_ts4.07.2021.2022.pre.dat.nc）时间范围为 2021—2022 年。数据集共包含 2 个文件：
\n本数据下载自英国东安格利亚大学气候研究中心（CRU）官方网站（https://crudata.uea.ac.uk/cru/data/hrg/），数据版本为 CRU TS 4.07。
", "ds_process_way": "CRU TS 4.07 数据集由英国东安格利亚大学气候研究中心制作发布，基于全球气象台站观测资料，通过对逐月气候异常值进行空间插值，并结合 1961—1990 年气候态平均值，生成覆盖全球陆地的 0.5°×0.5° 逐月格点数据，包含降水（pre）、潜在蒸散发（pet）等变量。研究团队从 CRU 官网下载了潜在蒸散发（1901—2022 年）和降水（2021—2022 年）两个变量的 NetCDF 格式数据，用于后续干旱与热浪复合事件相关分析，未对原始数据做进一步加工处理。
", "ds_quality": "CRU TS 是国际上广泛使用的全球气候格点数据集，数据来源权威、处理规范，经过严格的质量控制与同质化检验，时间跨度长、覆盖全球陆地，可靠性高，被大量气候变化研究引用。其局限在于：该数据集由台站观测插值得到，在观测台站稀疏地区（如沙漠、高山等）插值精度相对有限；格点值代表 0.5°×0.5° 网格的平均状况，难以反映局地小尺度气候差异；此外，本数据集中降水变量仅覆盖 2021—2022 年，时间序列较短。
", "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": 1567658936, "ds_files_count": 2, "ds_format": "NetCDF文件", "ds_space_res": "0.5°（约55km）", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "WGS84", "ds_thumbnail": "6057e244-8ba4-4729-895e-6568581a32d7.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "CRU TS v 4.07数据引用方式：Harris et al. (2020) doi:10.1038/s41597-020-0453-3", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.15" ], "quality_level": 1, "publish_time": "2026-10-27 10:16:56", "first_publish_time": null, "last_updated": "2026-10-27 10:16:56", "protected": false, "protected_to": null, "lang": "zh", "cstr": null, "license": null, "extra": null, "files_shape": [ { "name": "cru_ts4.07.1901.2022.pet.dat.nc", "size": 1517886748, "is_dir": false }, { "name": "cru_ts4.07.2021.2022.pre.dat.nc", "size": 49772188, "is_dir": false } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "", "ds_abstract": "This dataset consists of monthly global climate data provided by the Climatic Research Unit at the University of East Anglia. The data includes two variables: potential evapotranspiration (PET) and precipitation, stored in NetCDF format. The spatial coverage covers the entire globe, with a spatial resolution of 0.5°×0.5° (approximately 55 km). The time range covers each month from 1901 to 2022. Specifically, the PET data (cru_ts4.07.1901.2022.pet.dat.nc) covers the period from 1901 to 2022, while the precipitation data (cru_ts4.07.2021.2022.pre.dat.nc) covers the same period. The dataset contains two files:\r\n\r\n- cru_ts4.07.1901.2022.pet.dat.nc: Monthly potential evapotranspiration data for the global land surface from 1901 to 2022.\r\n- cru_ts4.07.2021.2022.pre.dat.nc: Monthly precipitation data for the global land surface from 2021 to 2022.", "ds_source": "This data was downloaded from the official website of the Climate Research Unit at the University of East Anglia in England (https://crudata.uea.ac.uk/cru/data/hrg/). The version of the data used is CRU TS 4.07.", "ds_process_way": "The CRU TS 4.07 dataset was created by the Climate Research Centre at the University of East Anglia in the UK. It is based on global meteorological station observations. By performing spatial interpolation on monthly climate anomalies, and combining these data with the average climate conditions from 1961 to 1990, a monthly grid data set covering all continents at a scale of 0.5°×0.5° was generated. This dataset includes variables such as precipitation and potential evapotranspiration. The research team downloaded NetCDF format data for both potential evapotranspiration (from 1901 to 2022) and precipitation (for the years 2021–2022) from the CRU website. These data were used for subsequent analyses related to drought and heatwave combinations, without any further processing of the raw data.", "ds_quality": "CRUTS is a globally recognized dataset for global climate data. The data comes from authoritative sources, and has undergone rigorous quality control and homogenization processes. It covers a wide range of time periods and includes information on all land areas around the world, making it highly reliable. This dataset is frequently used in research related to climate change. However, its limitations include the following: the data is derived from interpolations based on station observations, which means that the interpolation accuracy is limited in areas with sparse observation stations, such as deserts and mountainous regions. Additionally, the gridded values represent the average conditions within a 0.5°×0.5° grid, which makes it difficult to capture local climate variations. Furthermore, the precipitation data in this dataset only covers the years 2021–2022, resulting in a short time series.", "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": [ "CRU TS-4.07", "蒸发", "降水", "全球" ], "ds_subject_tags": [ "大气科学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 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, 2020, 2021, 2022 ], "ds_contributors": [ "李涛" ], "ds_meta_authors": [ "李涛" ], "ds_managers": [ "包安明" ], "category": "气象" }