Breaching 1.5°C: Give me the odds

Abstract

This paper develops a probability framework to convey the uncertainty surrounding predictions of when the temperature thresholds established by the Paris Agreement will be breached. The motivation for reporting probabilities is that they enable policymakers to make timely and informed decisions. The method can be summarised as predicting future temperatures under different scenarios and calculating the proportion of predicted outcomes that exceed the specified limits. These probabilities can be computed for different time horizons and updated as new data become available. We illustrate our method using two forecasting mechanisms. The first model links temperature anomalies to effective radiative forcings, enabling the assessment of how emissions scenarios affect breaching probabilities. The second model relies solely on the time series dynamics of observed temperatures and does not require additional inputs for forecasting. Our methodology is simple to implement and can be readily adapted to more complex climate models. We encourage climate model developers to include probabilities of breaching these limits in their reports.

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Download the paper (gold open access) here.

The code and data used to produce the results in the paper are available at the GitHub repository here.

Continuously updated results can be accessed via the dashboard here.

The working paper version is available for free (open access) here. But a much nicer looking version is available at the manuscript repository here. This version is done using Quarto and contains links to the code and data used in the paper. The paper is forthcoming in the International Journal of Forecasting.

Recommended citation

Text

Vera-Valdés, J. Eduardo, and Olivia Kvist. 2026. “Breaching 1.5°C: Give Me the Odds.” International Journal of Forecasting. Forthcoming. DOI: 10.1016/j.ijforecast.2026.07.004.

BibTex

@article{vera-valdés2026,
  author = {Vera-Valdés, J. Eduardo and Kvist, Olivia},
  title = {Breaching {1.5°C:} {Give} Me the Odds},
  journal = {International Journal of Forecasting},
  date = {2026-08-10},
  url = {https://doi.org/10.1016/j.ijforecast.2026.07.004},
  doi = {10.1016/j.ijforecast.2026.07.004
}