This record is Data product 1 of GHM_drought and provides observation-based historical drought indices for 1961–2024, together with multi-model ensemble-mean projections and inter-model uncertainty ranges for 2025–2100 based on 16 bias-corrected CMIP6 models. Individual-model SPEI, SPI, and EDDI projections for 2025–2100 are provided in Data products 2–4, 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 (Jan. 10, 2026)

2. Content of the dataset

This dataset comprises the following three types of data:

(1) SPI_{accumulation timescale}.zip: Each timescale zip file contains 6 Standardized Precipitation Index datasets in NetCDF format, comprising 3 scenarios (Historical+SSP1-2.6, Historical+SSP2-4.5, and Historical+SSP5-8.5) × 2 data types (index values and uncertainty ranges).

(2) EDDI_{accumulation timescale}.zip: Each timescale zip file contains 6 Evaporative Demand Drought Index datasets in NetCDF format, comprising 3 scenarios (Historical+SSP1-2.6, Historical+SSP2-4.5, and Historical+SSP5-8.5) × 2 data types (index values and uncertainty ranges).

(3) SPEI_{accumulation timescale}.zip: Each timescale zip file contains 6 Standardized Precipitation Evapotranspiration Index datasets in NetCDF format, comprising 3 scenarios (Historical+SSP1-2.6, Historical+SSP2-4.5, and Historical+SSP5-8.5) × 2 data types (index values and uncertainty ranges).

3. Details of the variables in the file

Each index value NetCDF file contains the following four variables:

(1) lat: Latitude dimension, measured in degrees (°).

(2) lon: Longitude dimension, measured in degrees (°).

(3) time: Time dimension.

(4) spi/eddi/spei: Drought index variable with dimensions (time, lat, lon). 

Each uncertainty range NetCDF file contains the following four variables:

(1) lat: Latitude dimension, measured in degrees (°).

(2) lon: Longitude dimension, measured in degrees (°).

(3) time: Time dimension.

(4) spi_std/eddi_std/spei_std: Inter-model standard deviation 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.

5. Citation

When using the GHM_drought dataset in your research, please cite the associated Data Descriptor:

Ji, J., Miao, C., Hu, J. et al. A global dataset of multiple meteorological drought indices for 1961–2100. Scientific Data (2026). https://doi.org/10.1038/s41597-026-08068-4