Extreme heat risk and the potential implications for the scheduling of football matches at the 2026 FIFA World Cup
Bibliographic record
Abstract
Climate change is making extreme heat events more frequent and intense. This negatively impacts many aspects of society, including organised sport. As the world's most watched sporting event, the FIFA World Cup commands particular attention around the threat of extreme heat. The 2022 tournament in Qatar was moved from summer to winter in response to this threat, and now attention turns to the 2026 tournament in North America with extreme heat risk across many of the 16 host locations. We examine this risk by modelling wet bulb globe temperature (WBGT) - a widely used measure of heat stress - for the host locations using hourly meteorological data for the period 2003-2022. Our results show that 14 out of 16 host locations exceed WBGTs of 28 °C, with four (nine) exceeding this threshold more than half the time during afternoons across the mean (hottest year) of the 20 year record. This threshold is important as the level beyond which some football governing bodies recommend match delay or postponement. A climatically sound argument is therefore presented to reschedule kick-off times outside the hottest afternoon hours for the host locations with highest heat risk at which no indoor air conditioned environment exists - primarily Miami and Monterrey, but also Philadelphia, Kansas City, Boston and New York. This study highlights the need to carefully assess heat risk ahead of major sporting events to help inform any potential interventions needed in the scheduling of matches and competitions in a warming climate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".