# The forecast competition leaves its data open.

ECMWF released JJA 2026 forecast files and regional scores for an open framework comparing AI and hybrid subseasonal weather systems.

Canonical: https://brightaifuture.com/discoveries/ecmwf-ai-weather-quest
Format: discovery
Source publication: 2026-09-14
Bright publication: 2026-09-19
Substantive update: None recorded
Evidence and review: Deployed; confidence: high; approved; ai-assisted. AI-assisted editorial comparison with the cited primary sources, explicit evidence limits, and held alternatives. Publication authorized by the site owner on 2026-09-19; no human source review or independent replication is claimed.

## The human problem

Communities and weather services need to know whether a promising model works at the lead times and in the regions they care about.

## The prior constraint

Forecast claims are hard to compare when systems use different variables, periods, and score definitions.

## AI’s actual role

Participating machine-learning and hybrid systems made real-time forecasts under common rules for temperature, pressure, and precipitation.

## The documented result

The public dataset covers common days 19–25 and 26–32 lead windows and publishes forecast files and regional scoring for JJA 2026.

## Why it may matter

Shared evaluation lets weather agencies and researchers inspect where skill appears rather than relying on a single global headline.

## Limitations

Benchmark skill does not make a forecast actionable in every region.

A single season is not long-term operational validation.

Warnings and public decisions remain institutional and human work.

## Unresolved questions

How stable are rankings across seasons and extremes?

Which regions have too little observation data for confident scoring?

How will participating systems document revisions?

## Provenance and history

{
  "dates": {
    "eventDate": "2026-09-14",
    "publicationDate": "2026-09-14",
    "captureDate": "2026-09-19",
    "lastReviewedDate": "2026-09-19"
  },
  "provenance": {
    "origin": "editorial",
    "externalId": "https://www.ecmwf.int/en/forecasts/dataset/ai-weather-quest-sub-seasonal-forecasts"
  },
  "revisions": [
    {
      "id": "revision:sept26-awq-01",
      "recordedAt": "2026-09-19",
      "summary": "Initial open-benchmark draft.",
      "sourceIds": [
        "source-awq-dataset",
        "source-awq-scores"
      ]
    }
  ],
  "corrections": []
}

## Original sources

- [AI Weather Quest - Sub-seasonal forecasts](https://www.ecmwf.int/en/forecasts/dataset/ai-weather-quest-sub-seasonal-forecasts)
- [JJA 2026 period - AI Weather Quest](https://confluence.ecmwf.int/spaces/AWQ/pages/673339713/JJA%2B2026%2Bperiod)

## Continue exploring

- [Planet](https://brightaifuture.com/worlds/planet)
- [Open intelligence](https://brightaifuture.com/worlds/open)
- [How much earlier could we understand what is coming?](https://brightaifuture.com/threads/weather)
- [What changes when powerful models become open-weight?](https://brightaifuture.com/threads/open)