Climate forecast: Seasonal
A global dataset at 0.25° resolution providing bias-corrected and downscaled multi-system seasonal forecasts with a 180-day outlook.
The dataset is based on four Copernicus Climate Change Service (C3S) seasonal forecast systems, combined into a single multi-system seasonal forecast, and post-processed to ensure consistency with observed climate statistics.
Specifications
Section titled “Specifications”| Specification | Description |
|---|---|
| Experiment | Seasonal Forecast |
| Forecast systems | Four systems originally provided by the Copernicus Climate Change Service, bias-corrected against observations and statistically downscaled: ECMWF (51 members), Météo-France (50 members), Deutscher Wetterdienst (50 members), Euro-Mediterranean Center on Climate Change (50 members). A multi-system ensemble is produced by combining all four systems. |
| Reference dataset | ERA5 reanalysis |
| Spatial resolution | 0.25° x 0.25° Global |
| Temporal resolution | Daily values over a 180-day forecast horizon |
| Variables | Four essential variables (others available on demand): Mean 2m temperature, Maximum 2m temperature, Minimum 2m temperature, Total precipitation |
| Delivery | Once a month on the 15th. |
| Format | NetCDF, other formats possible depending on data volume |
Supplementary information
Section titled “Supplementary information”The seasonal forecast dataset is statistically bias-corrected and downscaled using a quantile-mapping approach based on the CDF-t (Cumulative Distribution Function transform) method, applied independently for each lead month and grid point.
Key methodological characteristics:
- Probabilistic bias correction: all ensemble members are treated as equally likely and jointly used to estimate forecast probability distributions.
- Lead-time-dependent correction: bias correction is performed separately for each monthly lead time to account for forecast drift.
- Non-stationarity handling: the CDF-t method allows model and observational distributions to evolve over time, avoiding the stationarity assumption of standard bias-correction approaches.
- ERA5-consistent climatology: forecast probabilities are computed relative to ERA5-based climatologies derived from the hindcast period.
The reference for the original seasonal forecast datasets is :
Copernicus Climate Change Service, Climate Data Store, (2018): Seasonal forecast daily and subdaily data on single levels. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.181d637e
Availability
Section titled “Availability”The data can be delivered according to your specifications, contact us for a quote.
See all our seasonal and sub-seasonal forecast data.