Overview

All forecasts submitted to the AI Weather Quest are openly available via the AI Weather Quest Data Portal. Participants submit quintile-based probabilistic forecasts for near-surface air temperature (tas), mean sea level pressure (mslp), and accumulated precipitation (pr), targeting lead times of days 19 to 25 (week 3) and 26 to 32 (week 4). This guide explains how to download, extract, and use individual forecast files or archives.

The following teams webpage contains information regarding individual teams and their associated models.

Data publication timetable

The first year of the AI Weather Quest is divided into four 13-week competitive periods. After each period, and following the end-of-period webinar and forecast evaluation, submitted forecasts are made publicly available following the schedule below (where each acronym refers to the forecasted months, i.e. SON is September, October, November):

Portal directory structure

Individual files

Forecasts are organised in two ways:

1. Forecast initialisation date (by_fc_date)
2. Participating team (by_team)

The following directory structures are followed:

• by_fc_date >> by_team >> by_model >> forecasts
• by_team >> by_model >> by_fc_date >> forecasts

A separate NetCDF file is provided for each forecasted variable and lead time submitted by a team. 

Zip/Tar Archives

For more efficient downloading, we also provide .tar.gz archives containing all forecasts either:

• submitted by a given team (by_team_zipfiles), or
• issued for a given initialisation date (by_fc_date_zipfiles) 

Filename conventions

Each forecast file is in a NetCDF format with the following naming convention:

[variable]_[fc_init_date]_p[fc_window]_[teamname]_[modelname].nc 

where: 

• variable: The forecasted variable. Options are:
  • 'tas': Near-surface temperature (weekly-mean)
  • 'mslp': Mean sea level pressure (weekly_mean)
  • 'pr': Precipitation (weekly-accumulated)
• fc_init_date: The forecast initialisation date in format YYYYMMDD (e.g., ‘20250515’ for 15th May 2025).
• fc_window: The selected forecasting window. Valid options are:
  • '1': Weekly-mean forecasts for days 19 to 25 inclusive.
  • '2': Weekly-mean forecasts for days 26 to 32 inclusive.
• teamname : The associated team name with the submitted forecast.
• modelname: The associated model name with the submitted forecast.

Forecast download

Via the web browser

Click on individual files or zip/tar archives to download. Files will download automatically.

Via Linux/Unix terminal

To download forecasts in a Linux environment we recommend using wget or curl. When using either set of functions you will require the top-level URL of the portal (https://data.ecmwf.int/ai-weatherquest/) and the full directory path to the requested file.

Directory paths to individual forecasts are as follows: 

• Categorised by forecast initialisation date: 

[ROOT]/by_fc_date/[fc_init_date]/[teamname]/[modelname]/[forecast_file]

• Categorised by team:

[ROOT]/by_team/[teamname]/[modelname]/[fc_init_date]/[forecast_file]

Additionally, to download all forecasts submitted by a certain team or for a particular forecast initialisation date, use the following directory paths:

• Forecast initialisation date:

[ROOT]/by_fc_date_zipfiles/[fc_init_date].zip

• Team:

[ROOT]/by_team_zipfiles/[teamname].zip

The following are examples use the wget functionality:

Download near-surface temperature forecasts initialised on the 14th August 2025, for the first forecasting period, and submitted by team AIFS under the model name AIFSgaia:

wget https://data.ecmwf.int/ai-weatherquest/by_fc_date/20250814/AIFS/AIFSgaia/tas_20250814_p1_AIFS_AIFSgaia.nc

Download all forecasts submitted by team AIFS throughout the competition:

wget https://data.ecmwf.int/ai-weatherquest/by_team_zipfiles/AIFS.tar.gz

Extracting Archives

Finally, we recommend using the following tar command to unzip zip files.

tar -czf [zip_file] [new_directory]

tar -czf AIFS.tar.gz AIFS_forecasts/

Forecast file content

Licence information

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full licence terms can viewed here. 

Contact / support information

For support with accessing AI WQ sub-seasonal forecast data, please use one of the following communication channels:

• Public forum.
• Email: aiweatherquest@ecmwf.int

Dataset citation / DOI

When using this dataset, please cite the dataset DOI (provided below) and the following reference:

Loegel, O., Talib, J., Vitart, F., Hoffmann, J. and Chantry, M., 2025. The AI Weather Quest: an international competition for sub-seasonal forecasting with AI. Machine Learning: Earth, 1(1), p.010701.