CHM_PRE_SSP, a new bias-corrected daily CMIP6 precipitation projection dataset over the Chinese mainland

作者：Jinlong Hu, Chiyuan Miao, Jiachen Ji · 发表于：Zenodo (CERN European Organization for Nuclear Research) · 年份：2026 · DOI：10.5281/zenodo.21535096 · 研究领域：Environmental science、Climatology、Meteorology、Statistics

1. Description Dataset name: CHM_PRE_SSP CHM_PRE_SSP is a long-term, bias-corrected precipitation projection dataset developed for the Chinese mainland. It contains historical simulations from 1961 to 2014 and future projections from 2015 to 2100 under three Shared Socioeconomic Pathway scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The dataset was produced from 28 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs) and is provided at a spatial resolution of 0.1° and at daily, monthly, and annual temporal resolutions. The CHM_PRE V2 daily gridded precipitation dataset was used as the observational reference during the historical period. CHM_PRE V2 was developed from long-term observations at 3,746 meteorological stations and 11 precipitation-related covariates, using an improved inverse distance weighting method combined with the machine learning algorithm. Singularity Stochastic Removal (SSR) was applied to address the large number of tied zero-precipitation values and improve the representation of precipitation occurrence. Quantile Delta Mapping (QDM) was then used to correct precipitation distributions while preserving the relative changes projected by the GCMs. The correction was performed independently for each calendar month and grid cell. Independent validation showed that CHM_PRE_SSP substantially outperformed the raw CMIP6 simulations in representing annual precipitation amount, wet-day frequency, precipitation extremes, and dry- and wet-...