Global Drought Experimental Dataset using Multi-Scenario Fusion Methods (2015-2100)
CUI Jingwen1,2LIU Yangxiaoyue*1XI Yongshi3ZHAO Guanfeng4
1 Institute of Geographic Sciences and Natural Resources Research，Chinese Academy of Sciences，Beijing 100101，China2 University of Chinese Academy of Sciences，Beijing 100049，China3 Xizang Autonomous Region Agro-products Quality and Safety Inspection and Testing Center，Lhasa 850000，China4 Xizang Autonomous Region Agricultural Technology Extension Service Center，Lhasa 850000，China
DOI:10.3974/geodb.2026.05.02.V1
Published:May. 2026
Visitors：15 Data Files Downloaded：0
Data Downloaded： 无 Citations：
Key Words：
drought prediction,multi-scenario projections,drought indices,global,data fusion
Abstract：
Under the background of global climate change, droughts are becoming increasingly frequent, spatially extensive, and intense, posing a significant threat to the stability of natural and social systems. To develop a reliable global multi-scenario future drought prediction dataset, this study employs 18 Earth system models from the Coupled Model Inter-comparison Project Phase 6 (CMIP6), selecting four key climate variables - precipitation (mm/month), moisture in the upper portion of the soil column (% volumetric), near-surface air temperature (℃), and potential evapotranspiration (PET, mm/month) - with unified temporal and spatial resolutions to calculate four representative drought indices: the Standardized Precipitation Evapotranspiration Index (SPEI), the Drought Severity Index (DSI), the Temperature Condition Index (TCI), and the Standardized Soil Moisture Index (SSMI). Five major historical drought events were selected to validate the predictive accuracy of each index. The results indicate that DSI and SSMI outperform the other indices in identifying drought severity and spatial extent, more accurately capturing the spatiotemporal evolution of future droughts. For SPEI, SSMI, and DSI, lower values indicate greater drought severity (≤−1.0: moderate drought; ≤−1.5: severe drought; ≤−2.0: extreme drought); for TCI, lower values indicate stronger heat stress (<50%: high heat stress). The dataset contains monthly values of four representative drought indices under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios spanning 2015-2100, at a spatial resolution of 0.5°. The dataset is archived in .tif format, and consists of 16,512 data files with data size of 7.45 GB (compressed into 8 files, 4.86 GB).
Foundation Item：
National Natural Science Foundation of China (42571539); Ministry of Science and Technology of P. R. China (2022YFF0711603)
Data Citation：
CUI Jingwen, LIU Yangxiaoyue*, XI Yongshi, ZHAO Guanfeng. Global Drought Experimental Dataset using Multi-Scenario Fusion Methods (2015-2100)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2026. https://doi.org/10.3974/geodb.2026.05.02.V1. .
References：
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Data Product:
| ID |
Data Name |
Data Size |
Operation |
| 1 |
DSI_SSP126_245.rar |
747803.71KB |
|
| 2 |
DSI_SSP370_585.rar |
698633.93KB |
|
| 3 |
SPEI_SSP126_245.rar |
717255.71KB |
|
| 4 |
SPEI_SSP370_585.rar |
714898.81KB |
|
| 5 |
SSMI_SSP126_245.rar |
706859.48KB |
|
| 6 |
SSMI_SSP370_585.rar |
709154.42KB |
|
| 7 |
TCI_SSP126_245.rar |
408973.12KB |
|
| 8 |
TCI_SSP370_585.rar |
395945.43KB |
|