全球气候变化加剧水文循环，极端降水事件频发，降水集中度指标是诊断降水时空分布特征的关键工具。现有研究存在站点观测碎片化、历史基准与未来预估脱节等问题。本数据集整合 1961-2022 年中国地面观测与格点观测数据，以及 2015-2100 年 4 种 SSP 情景下经统计降尺度的 CMIP6 预估数据，空间分辨率 0.25°，构建包含降水集中度（PCD）、降水集中期（PCP）、日降水集中度指数（DPCI）、月降水集中度指数（MPCI）4 项核心指标的时空连续数据集（MPCID）。
经站点数据验证，PCD 误差最小、相关性最优，数据集可支撑中国降水时空分布规律、水文与农业气候影响评估、适应性管理策略制定等研究。", "ds_source": "
1. 地面日降水数据（1961-2020）：中国气象局中国地面气候资料日值数据集（V3.0），经质量控制与均一性检验，筛选 651 个站点；\n
2. 格点观测数据（1961-2022）：CN05.1 格点降水数据集，0.25°×0.25°，中国科学院气候变化研究中心发布；\n
3. 气候模式数据：24 个 CMIP6 全球气候模式输出降水数据，覆盖 SSP1-2.6、SSP2-4.5、SSP3-7.0、SSP5-8.5 四种情景。", "ds_process_way": "
1. 数据预处理：对站点数据进行质量控制、缺值插补；对 CMIP6 模式数据进行统一分辨率重采样、日历格式转换；\n
2. 空间插值：采用 KD-Tree + 反距离权重（IDW）将站点数据插值为格点数据；\n
3. 统计降尺度：采用分位数映射（QM）、分位数增量映射（QDM）统计降尺度方法对 CMIP6 数据进行偏差校正，优选重采样后使用分位数增量映射方法为最优框架；\n
4. 指标计算：基于向量分析法计算 PCD、PCP，基于洛伦兹曲线计算 DPCI，基于方差法计算 MPCI；\n
5. 精度验证：采用 MAE、RMSE、BIAS、CORR、IVS、TS 等指标开展多维度验证。", "ds_quality": "
1. 历史期验证：PCD 的 MAE=0.034、RMSE=0.042、CORR=0.902，精度最优；PCP 相关性高但误差较大；DPCI 误差可控但日尺度相关性有限；MPCI 对极端降水敏感性较低；\n
2. 未来情景验证：四种 SSP 情景下，PCD 的 MAE 为 0.094-0.096，RMSE 为 0.122-0.125，性能稳定；resample-QDM 降尺度方法误差最小；\n
3. 空间验证：数据集能准确刻画中国降水集中度 “北高西低” 等空间分异特征，复杂地形区结果需结合站点密度审慎解读。", "ds_acq_start_time": "1961-01-01 00:00:00", "ds_acq_end_time": "2100-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 135.0, "ds_acq_lat_south": 18.0, "ds_acq_lon_west": 73.5, "ds_acq_lat_north": 53.5, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 151215967, "ds_files_count": 2, "ds_format": "*.nc, *.csv", "ds_space_res": "0.25°（约 30km）", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "3b772081-0169-41b2-8c1c-529069194ada.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.15", "170.45", "170.55" ], "quality_level": 0, "publish_time": "2026-04-28 11:29:45", "last_updated": "2026-05-09 19:54:59", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.PRECIPITATION.DB7311.2026", "i18n": { "en": { "title": "Multi-precipitation concentration indicators dataset for mainland China from 1961 to 2100", "ds_format": "*.nc, *.csv", "ds_source": "
1. Daily surface precipitation data (1961-2020): China Meteorological Administration's daily surface climate data set (V3.0), after quality control and uniformity testing, 651 stations were selected;\r\n
2. Grid point observation data (1961-2022): CN05.1 grid point precipitation data set, 0.25 ° × 0.25 °, issued by the Climate Change Research Center of the Chinese Academy of Sciences;\r\n
3. Climate model data: 24 CMIP6 global climate models output precipitation data, covering four scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5.", "ds_quality": "
1. Historical verification: PCD has the best accuracy with MAE=0.034, RMSE=0.042, CORR=0.902; PCP has high correlation but significant error; DPCI error is controllable, but the correlation on a daily scale is limited; MPCI has low sensitivity to extreme precipitation;\r\n
2. Future scenario verification: Under four SSP scenarios, the MAE of PCD is 0.094-0.096, and the RMSE is 0.122-0.125, indicating stable performance; The resample QDM downscaling method has the smallest error;\r\n
3. Spatial validation: The dataset can accurately depict the spatial differentiation characteristics of precipitation concentration in China, such as \"high in the north and low in the west\". The results of complex terrain areas need to be interpreted carefully in conjunction with station density.", "ds_ref_way": "", "ds_abstract": "
Global climate change intensifies the hydrological cycle, leading to frequent extreme precipitation events. The precipitation concentration index is a key tool for diagnosing the spatiotemporal distribution characteristics of precipitation. The existing research has problems such as fragmented site observations and a disconnect between historical benchmarks and future estimates. This dataset integrates ground observation and grid observation data from China from 1961 to 2022, as well as statistically downscaled CMIP6 estimation data from four SSP scenarios from 2015 to 2100, with a spatial resolution of 0.25 °. It constructs a spatiotemporal continuous dataset (MPCID) that includes four core indicators: precipitation concentration (PCD), precipitation concentration period (PCP), daily precipitation concentration index (DPCI), and monthly precipitation concentration index (MPCI).
Through site data verification, PCD has the smallest error and the best correlation. The dataset can support research on the spatiotemporal distribution of precipitation in China, hydrological and agricultural climate impact assessment, and adaptive management strategy formulation.", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
1. Data preprocessing: Quality control and missing value imputation of site data; Perform unified resolution resampling and calendar format conversion on CMIP6 mode data;\r\n
2. Spatial interpolation: KD Tree+inverse distance weight (IDW) is used to interpolate station data into grid data;\r\n
3. Statistical downscaling: Quantile mapping (QM) and quantile incremental mapping (QDM) statistical downscaling methods are used to correct bias in CMIP6 data. The optimal framework is to use quantile incremental mapping after resampling;\r\n
4. Indicator calculation: Calculate PCD and PCP based on vector analysis, calculate DPCI based on Lorenz curve, and calculate MPCI based on variance method;\r\n
5. Accuracy verification: Multi dimensional verification is conducted using indicators such as MAE, RMSE, BIAS, CORR, IVS, TS, etc.", "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": [ "降水集中度", "CMIP6", "统计降尺度", "SSP", "情景", "中国" ], "ds_subject_tags": [ "大气科学", "地理学", "水文学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国", "青藏高原", "西北干旱区", "东部平原" ], "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, 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": "张栋洋", "email": "zdy_sxhz@163.com", "work_for": "兰州交通大学", "country": "中国" }, { "true_name": "李雪梅", "email": "lixuemei@mail.lzjtu.cn", "work_for": "兰州交通大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张栋洋", "email": "zdy_sxhz@163.com", "work_for": "兰州交通大学", "country": "中国" } ], "ds_managers": [ { "true_name": "李雪梅", "email": "lixuemei@mail.lzjtu.cn", "work_for": "兰州交通大学", "country": "中国" } ], "category": "水文" }