A High-Resolution (1km) Future Drought Prediction Dataset via Causal Deep Learning (SPEI, 2023–2030)

This dataset provides high-resolution medium- to long-term drought predictions specifically covering Hubei Province, China, spanning from 2023 to 2030. Generated utilizing advanced causal deep learning methodologies, it features a fine spatial resolution of 1 kilometer and includes the Standardized Precipitation Evapotranspiration Index (SPEI) across multiple timescales: 1-month (SPEI1), 3-month (SPEI3), 6-month (SPEI6), and 12-month (SPEI12). These diverse timescales effectively capture a wide spectrum of drought conditions, from short-term meteorological anomalies to long-term hydrological deficits, making the data essential for regional climate resilience planning, agricultural risk assessment, and proactive water resource management across the province. By employing a causal deep learning framework rather than relying solely on traditional statistical forecasting, this approach explicitly models the underlying cause-and-effect mechanisms driving climate extremes. This ensures that future projections remain physically robust and reliable under shifting environmental conditions, tailored to the specific geographical and climatic context of Hubei Province. The development of this dataset was financially supported by the Wuhan Natural Science Foundation Exploration Project (Chenguang Project) under grant number 2024040801020279. Researchers and practitioners utilizing this drought prediction data for academic or applied studies are kindly requested to properly cite the dataset and acknowledge this funding source in their related publications and research outputs.