GHM_drought: A global dataset of multiple meteorological drought indices for 1961–2100 (Data product 3: Individual-model SPI from 16 CMIP6 models)

This record is Data product 3 of GHM_drought and provides individual-model SPI projections for 2025–2100 from 16 bias-corrected CMIP6 models under SSP1-2.6, SSP2-4.5, and SSP5-8.5. Data product 1 provides observation-based historical drought indices for 1961–2024, together with multi-model ensemble-mean projections and inter-model uncertainty ranges for 2025–2100, while Data products 2 and 4 provide the corresponding individual-model SPEI and EDDI projections, respectively. Associated records: Data product 1, https://doi.org/10.5281/zenodo.18045718; Data product 2, https://doi.org/10.5281/zenodo.21491403; Data product 3, https://doi.org/10.5281/zenodo.21523380; Data product 4, https://doi.org/10.5281/zenodo.21540518. 1. Description Dataset name: GHM_drought Summary: The GHM_drought dataset is a new Global Meteorological Drought Dataset at 0.5° spatial resolution for the period 1961–2100, derived from the Climatic Research Unit (CRU) dataset and 16 bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) models. This dataset calculates Standardized Precipitation Index (SPI), Evaporative Demand Drought Index (EDDI), and Standardized Precipitation Evapotranspiration Index (SPEI) based on a unified framework, ensuring consistency and comparability among the indices. Additionally, the dataset provides multiple accumulation timescales (1, 3, 6, 9, 12 months, and 1 year) and multiple scenarios, including historical (1961–2024) and future Shared Socioeconomic Pathway (SSP) scenarios (2025–2100) (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Crucially, the dataset provides uncertainty ranges for future projections to enhance reliability. Validation results demonstrate that the GHM_drought captures historical drought events robustly and maintains high consistency with existing benchmark datasets. For future projections, the applied threshold-based quantile mapping method effectively corrects systematic biases. The GHM_drought aids global drought monitoring and projection, thereby supporting climate risk assessment and adaptation. Latest version: Version 1 (Jul. 25, 2026) 2. Content of the dataset This dataset contains single-model Standardized Precipitation Index (SPI) data at six accumulation timescales: 1 month, 3 months, 6 months, 9 months, 12 months, and 1 year. SPI_{accumulation timescale}.zip: Each accumulation-timescale archive contains 48 SPI datasets in NetCDF format, comprising 16 CMIP6 models under three future scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Each NetCDF file represents the SPI output from one individual CMIP6 model under one future scenario for the period 2025–2100. The files are named according to the following convention: SPI_{accumulation timescale}_{scenario}_{model}.nc For example: SPI_3-month-scale_SSP245_ACCESS-CM2.nc and SPI_1-year-scale_SSP585_MPI-ESM1-2-HR.nc 3. Details of the variables in the files Each single-model SPI NetCDF file contains the following four variables: (1) lat: Latitude coordinate, measured in degrees (°). (2) lon: Longitude coordinate, measured in degrees (°). (3) time: Time coordinate. (4) spi: Standardized Precipitation Index variable with dimensions (time, lat, lon). 4. Examples of utilization The NetCDF files can be accessed and processed using various software tools, including GIS applications such as ArcGIS Pro, visualization tools like Panoply, and programming libraries such as xarray in Python.