The global dataset on extreme precipitation exposure in industrial economic systems

1. Temporal Coverage of Data:The data collection periods are 2010, 2016-2035, and 2046-2065.2. Spatial Coverage and Projection:Spatial Coverage: GlobalLongitude: -180° - 180°Latitude: -90° - 90°Projection: GCS_WGS_19843. Disciplinary Scope:The data pertains to the fields of Earth Sciences and Geography.4. Data Volume:The total data volume is approximately 63.0 MB.5. Data Type:Raster (GeoTIFF)6. Thumbnail (illustrating dataset content or observation process/scene):·7. Field (Feature) Name Explanation:Name Explanation:EXP: ExposureVUL: VulnerabilityUnit of Measurement:Exposure: days-USDVulnerability: dimensionless8. Data Source Description:Population and Industrial Value Added Projection Data:Sourced from the project "Study on the Harmful Processes of Population and Economic Systems under Global Change" under the National Key R&D Program "Mechanisms and Assessment of Risks in Population and Economic Systems under Global Change," led by Researcher Sun Fubao at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences.Meteorological Data:From the Fifth International Coupled Model Intercomparison Project (CMIP5) published by the United Nations Intergovernmental Panel on Climate Change (IPCC), primarily including the MIROC5, MPI-ESM-LR, and HadGEM2-ES3 climate models.Statistical Data:From the World Development Indicators dataset of the World Bank and various national statistical agencies.9. Data Processing MethodsThe exposure of the industrial economy to extreme precipitation is defined as the product of the number of extreme precipitation days and the total industrial output value exposed in each grid cell.Where, INDEXPPR is the industrial economic exposure under extreme precipitation (unit: days-USD). R95p is the number of extreme precipitation days. IND_VALUE is the industrial output value.Extreme Precipitation Definition:Set 1961-1990 as the reference period and calculate the 95th percentile value of the annual wet-day (daily precipitation > 1mm) precipitation sequence to define the extreme precipitation threshold. According to the WMO extreme precipitation index definition, the number of days with daily precipitation greater than 25mm is defined as heavy rain days. If the local 95th percentile threshold is less than 25mm, it is set to 25mm. Selected three climate models (MIROC5, MPI-ESM-LR, HadGEM2-ES) to predict precipitation data for 2010-2050 under different climate change scenarios, using a multi-model ensemble averaging method for climate multi-model coupling.10. Applications and Achievements of the Dataseta. Primary Application Areas: This dataset is mainly applied in environmental protection, ecological construction, pollution prevention and control, and the prevention and forecasting of natural disasters.b. Achievements in Application (Awards, Published Reports, and Articles):Achievements: Published several academic articles that have enhanced the understanding of the vulnerability of global industrial economic systems under different Shared Socioeconomic Pathways (SSPs).

1. 数据时间覆盖范围：数据采集时段为2010年、2016-2035年及2046-2065年。
2. 空间覆盖与投影：空间覆盖：全球；经度：-180°至180°；纬度：-90°至90°；投影：GCS_WGS_1984。
3. 学科范围：数据涉及地球科学与地理学领域。
4. 数据量：总数据量约为63.0 MB。
5. 数据类型：栅格（GeoTIFF）。
6. 缩略图（展示数据集内容或观测过程/场景）：·
7. 字段（特征）名称解释：名称解释：EXP：暴露度；VUL：脆弱性；计量单位：暴露度：天-美元（days-USD）；脆弱性：无量纲。
8. 数据来源描述：人口与工业增加值投影数据：来源于中国科学院地理科学与资源研究所孙福宝研究员牵头的国家重点研发计划“全球变化下人口与经济系统风险形成机制及评估”项目；气象数据：来自联合国政府间气候变化专门委员会（IPCC）发布的第五次国际耦合模式比较计划（CMIP5），主要包括MIROC5、MPI-ESM-LR和HadGEM2-ES三个气候模型；统计数据：来自世界银行的世界发展指标数据集及各国统计机构。
9. 数据处理方法：工业经济对极端降水的暴露度定义为每个网格单元内极端降水日数与暴露的工业总产值的乘积。其中，INDEXPPR为极端降水下的工业经济暴露度（单位：天-美元）；R95p为极端降水日数；IND_VALUE为工业总产值。极端降水定义：以1961-1990年为参考期，计算年湿日（日降水量>1mm）降水序列的95百分位值作为极端降水阈值；根据世界气象组织（WMO）极端降水指数定义，日降水量大于25mm的天数定义为大雨日，若本地95百分位阈值小于25mm，则设为25mm；选取MIROC5、MPI-ESM-LR和HadGEM2-ES三个气候模型，预测不同气候变化情景下2010-2050年的降水数据，采用多模型集合平均法进行气候多模型耦合。
10. 数据集应用与成果：a. 主要应用领域：该数据集主要应用于环境保护、生态建设、污染防治及自然灾害预防与预报；b. 应用成果（奖项、已发表报告及论文）：成果：发表多篇学术论文，提升了对不同共享社会经济路径（SSP）下全球工业经济系统脆弱性的理解。