ArticleWeather and climate extremes2026
Unseen but increasing: recent changes in risk of extreme precipitation over Southern Africa and Southeast Asia.
Article in Weather and climate extremes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
There is evidence that rainfall extremes have become more intense and frequent over the last few decades, but it is difficult to assess these changes due to the limitations of our short observational records. We use the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach to (1) assess changes in extreme rainfall over Southern Africa and Southeast Asia over the last 40 years and (2) identify locations that have a high chance of breaking rainfall records. We find that extreme rainfall risk has already increased since 1981 during the rainy season in both regions, including a doubling of risk in some months for many major population centers such as Phnom Penh, Vientiane, Bangkok, Hanoi, Maseru, Johannesburg, Lilongwe, and Lusaka. The pattern of increasing risk of extreme rainfall is projected to increase further in the coming 20 years in the CMIP6 ensemble; yet UNSEEN estimates of changes from the last 20 years are already greater than these future projections in the Philippines, northern Mozambique, and northern Madagascar. Finally, we compare the UNSEEN ensemble to historical records to identify places that have "soft records" and are likely to see record-setting events. These places with increasing risks but no recent extremes are labeled as "sitting ducks" in today's climate. We find that much of Mozambique, the Philippines, and Laos would be considered "sitting ducks" for extreme precipitation in at least one month of the year. Disaster risk managers should use these types of large ensembles when estimating the risk of extremes in today's climate, in order to ensure that society is prepared for record breaking events. This approach can also be used for improving engineering design estimates of rainfall return periods and for stress-testing health system and disaster preparedness.
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