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": 0, "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": "2026-04-28 11:29:45", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.PRECIPITATION.DB7311.2026", "i18n": { "en": { "title_en": "Multi-precipitation concentration indicators dataset for mainland China from 1961 to 2100", "ds_format_en": "*.nc, *.csv", "ds_source_en": "1. Daily in-situ precipitation data (1961-2020): China Ground Climate Dataset (V3.0) from China Meteorological Administration, with quality control and homogeneity test, 651 stations selected;\r\n2. Gridded observation data (1961-2022): CN05.1 gridded precipitation dataset, 0.25°×0.25°, released by Climate Change Research Center, Chinese Academy of Sciences;\r\n3. Climate model data: Outputs of 24 CMIP6 global climate models, covering four scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5.", "ds_quality_en": "1. Historical period validation: PCD has MAE=0.034, RMSE=0.042, CORR=0.902, with the best accuracy; PCP has high correlation but large error; DPCI has controllable error but limited daily-scale correlation; MPCI has low sensitivity to extreme precipitation;\r\n2. Future scenario validation: Under four SSP scenarios, PCD has MAE of 0.094-0.096 and RMSE of 0.122-0.125, with stable performance; resample-QDM downscaling method has the smallest error;\r\n3. Spatial validation: The dataset can accurately characterize the spatial differentiation characteristics such as \"high in the north and low in the west\" of precipitation concentration in China. Results in complex terrain areas should be interpreted carefully combined with station density.", "ds_ref_way_en": "", "ds_abstract_en": "", "ds_time_res_en": "", "ds_acq_place_en": "", "ds_space_res_en": "", "ds_projection_en": "", "ds_process_way_en": "1. Data preprocessing: Quality control and missing value interpolation for station data; resampling to uniform resolution and calendar format conversion for CMIP6 model data;\r\n2. Spatial interpolation: Interpolating station data to grid data using KD-Tree combined with inverse distance weighting (IDW);\r\n3. Statistical downscaling: Bias correction of CMIP6 data using quantile mapping (QM), quantile delta mapping (QDM) and other methods, with resample-QDM selected as the optimal framework;\r\n4. Indicator calculation: Calculating PCD and PCP based on vector analysis, DPCI based on Lorenz curve, and MPCI based on variance method;\r\n5. Accuracy validation: Multi-dimensional verification using MAE, RMSE, BIAS, CORR, IVS, TS and other indicators.", "ds_ref_instruction_en": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0 (Creative Commons Attribution 4.0 International License)", "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" }