Global Bias-corrected Seasonal Forecast (SEAS5-BCSD) Archive 1981 To 2024 For Precipitation And Temperature

Project: Development of an operational, multisectoral, global drought hazard prediction system: Bias correction of operational global seasonal predictions - The overall objective of the OUTLAST project is to develop, in co-design with potential users, a global and operational drought forecasting system for rainfed agriculture, irrigated agriculture, water supply, and terrestrial and riverine ecosystems, and to implement it as a component of the WMO's Global Hydrological Status and Outlook System (HydroSOS). The sub-project of the Karlsruhe Institute of Technology (KIT) aims at developing bias-corrected and regionally adjusted seasonal ensemble forecasts, improving their performance and making them available to the consortium for subsequent derivation of drought hazard indicators. This will be done for all land areas on Earth (except Greenland and Antarctica), optimizing indicators in co-design with users. The basis of our work is the global seasonal forecasting system SEAS5 of ECMWF. Website: https://outlast-project.net
Summary: Seasonal forecasts provide valuable insights into upcoming conditions, offering forecast horizons of up to seven months. However, uncorrected seasonal forecasts often exhibit substantial biases and drifts, when compared to reference data like, e.g., ERA5. This makes post-processing essential for their use in analysis and downstream modelling.
This dataset provides bias-corrected and downscaled seasonal forecasts from the ECMWF SEAS5 system for precipitation and 2-meter temperature, covering the global land surface at 0.25° resolution. Using the Bias Correction and Spatial Disaggregation (BCSD) method, forecasts are corrected for biases and temporal drifts and include all SEAS5 ensemble members over the full hindcast period (1981–2016) and until 2024. The dataset supports lead times up to seven months and is suitable for a wide range of applications in water, energy, and agricultural sectors. Forecast quality has been evaluated using Brier Skill Scores and CRPSS, showing strong skill for temperature across most regions and for precipitation in tropical and humid areas.