高精度的降水数据对水文研究、农业、洪水预报、干旱监测和气候变化适应战略具有重要意义。本数据利用Bayesian model averaging method （BMA）融合9种降水产品，获取了西北干旱区（2.5′）2000-2024年月尺度降水数据。数据集包含的地理空间范围是西北干旱区。该数据集以nc文件格式存储，文件命名方式为pre_year，year表示年份。
", "ds_source": "数据源来自于9种降水产品，包括IMERG、 MSWEP、ERA5-Land、 WorldClim、CHIRPS、 FLDAS、 GLDAS、MERRA和 CMORPH。
", "ds_process_way": "贝叶斯模式平均（Bayesian model averaging）
", "ds_quality": "152个气象站的验证结果中位值如下：相关系数R2=0.68；相对绝对误差MAE=6.5mm、均方根误差RMSE=9.0mm，一致性指数d=0.86。 25个子流域的验证结果中位值如下：相关系数R2=0.52；绝对误差MAE=54.6mm、平均均方根误差RMSE=61.1mm，一致性指数d=0.57。
", "ds_acq_start_time": "2024-12-31 00:00:00", "ds_acq_end_time": "2024-12-31 00:00:00", "ds_acq_place": "西北干旱区", "ds_acq_lon_east": 107.0, "ds_acq_lat_south": 34.36666666666667, "ds_acq_lon_west": 73.4, "ds_acq_lat_north": 49.2, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 646306493, "ds_files_count": 26, "ds_format": "nc", "ds_space_res": "2.5′", "ds_time_res": "月", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "b88cfab0-1e9d-44bc-b198-fe8cf037ca16.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": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.5525" ], "quality_level": 3, "publish_time": "2025-11-19 08:52:49", "last_updated": "2025-11-19 08:52:49", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7006.2025", "i18n": { "en": { "title": "Monthly precipitation dataset for the arid region of Northwest China during 2000–2024", "ds_format": "", "ds_source": "The data source comes from 9 precipitation products, including IMERG, MSWEP, ERA5 Land, WorldClim, CHIRPS, FLDAS, GLDAS, MERRA, and CMORPH.
", "ds_quality": "The median values of the validation results from 152 meteorological stations are as follows: correlation coefficient R2=0.68; The relative absolute error MAE is 6.5mm, the root mean square error RMSE is 9.0mm, and the consistency index d is 0.86. The median values of the validation results for 25 sub basins are as follows: correlation coefficient R2=0.52; The absolute error MAE is 54.6mm, the average root mean square error RMSE is 61.1mm, and the consistency index d is 0.57.
", "ds_ref_way": "", "ds_abstract": "High precision precipitation data is of great significance for hydrological research, agriculture, flood forecasting, drought monitoring, and climate change adaptation strategies. This data utilizes Bayesian model averaging method (BMA) to fuse 9 precipitation products and obtain monthly precipitation data from 2000 to 2024 in the northwest arid region (2.5 ′). The geographic spatial range included in the dataset is the arid northwest region. This dataset is stored in NC file format, with the file naming convention being pre_year, where year represents the year.
", "ds_time_res": "月", "ds_acq_place": "Northwest arid region", "ds_space_res": "2.5′", "ds_projection": "", "ds_process_way": "Bayesian model averaging
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "降水" ], "ds_subject_tags": [ "水文气象学" ], "ds_class_tags": [], "ds_locus_tags": [ "西北干旱区" ], "ds_time_tags": [ 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 ], "ds_contributors": [ { "true_name": "阳勇", "email": "yy177@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "阳勇", "email": "yy177@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "阳勇", "email": "yy177@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "水文" }