Return period
The return period of an event is the average time between events at least that large, in a given climate. It is the inverse of the yearly chance: a 100-year flood or wind speed has a 1% chance of being reached or exceeded in any year. The size of the event for a given return period is its return level, for example the 100-year wind speed at a site.
What a 100-year event does not mean
Section titled “What a 100-year event does not mean”It does not mean once every 100 years. The chance is the same every year, so two 100-year events can come in consecutive years, and over a long period the chance of at least one adds up: 26% over 30 years, 63% over 100 years. This is what matters for an asset with a lifetime of decades.
Table 1. Chance of at least one event over a period, by return period: 1 − (1 − 1/T)N for a return period of T years over N years, in a climate that does not change.
| Return period | 1 year | 10 years | 30 years | 50 years |
|---|---|---|---|---|
| 10 years | 10% | 65% | 96% | 99.5% |
| 50 years | 2% | 18% | 46% | 64% |
| 100 years | 1% | 10% | 26% | 40% |
| 200 years | 0.5% | 5% | 14% | 22% |
| 1,000 years | 0.1% | 1% | 3% | 5% |
How return periods are estimated
Section titled “How return periods are estimated”Return levels come from extreme value statistics: a distribution is fitted to the largest values of each year, or of a long record, and read at the chosen chance (Coles, 2001). Return periods longer than the observed record, such as 1,000 or 10,000 years, need many years of data: our tropical cyclone dataset estimates them from 10,000 years of synthetic cyclones generated by the STORM model (Bloemendaal et al., 2020).
Return periods and climate change
Section titled “Return periods and climate change”A return period holds for one climate. As the climate changes, an event of a given size can become more or less frequent: the 100-year event of the past may come back more often in the future. Estimating return periods in a changing climate is a challenge in itself: the classic methods assume that the climate does not change over the record, and the record is short for rare events. New statistical methods estimate the chance of an extreme for each year and each scenario, combining climate model simulations with observations. One is ANKIALE (Robin et al., 2026): applied to the hottest three-day spell of each year over Europe (daily maximum temperature), it finds that in parts of North Africa the hottest event of 1940–2024 now has a return period of 2 to 5 years, where without human influence it would have exceeded 1,000 years.
Our datasets give return levels for different periods or time points, so the change can be read directly:
- Tropical cyclones: maximum wind speed for 28 return periods, from 10 to 10,000 years, for 1980–2017 and 2015–2050.
- Floods: flooded area for the 100-year and 200-year events, at present and in 2030, 2050 and 2080.
See all our climate projection data on the main site.
References
Section titled “References”- Robin, Y., Vrac, M., Ribes, A., Barbaux, O., & Naveau, P. (2026). A Bayesian statistical method to estimate the climatology of extreme temperature under multiple scenarios: the ANKIALE package. Geoscientific Model Development, 19, 2349–2372. https://doi.org/10.5194/gmd-19-2349-2026
- Bloemendaal, N., Haigh, I. D., de Moel, H., Muis, S., Haarsma, R. J., & Aerts, J. C. J. H. (2020). Generation of a global synthetic tropical cyclone hazard dataset using STORM. Scientific Data, 7, 40. https://doi.org/10.1038/s41597-020-0381-2
- Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. https://doi.org/10.1007/978-1-4471-3675-0