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Quantifying Uncertainty in the Perceived Risk of Unprecedented Rainfall
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DOI：10.1029/2026EF008121.png)
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Abstract
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While extreme rainfall is projected to intensify with rising global temperatures, natural variability can obscure the detectability of long-term changes at local scales. Given current extreme rainfall assessments largely rely on historical observations, the relative severity of historical events can influence perceived present-day risk. With a focus on New Zealand, here we use 3,226 years of high-resolution (∼50 km) model simulations of the recent past climate to quantify uncertainty in perceived extreme rainfall risk due to internal variability. By repeatedly sampling 100-year periods of initial-condition model simulations and identifying the most severe rainfall events to occur at each grid cell, we examine the rarity and intensity of synthetic “worst-in-century” (WIC) events as a proxy for the worst rainfall event in “living memory.” Depending on the period, WIC intensity ranges from <70% to >200% of the “true” 1-in-100-year rainfall event estimated from the full data set, with corresponding WIC return periods spanning 25 to >5,000 years. The co-occurrence of significant events nationwide within the same century can also differ substantially. These findings highlight the consequences of sampling uncertainty when inferring the potential risks of record-shattering rainfall from historical experiences alone, presenting challenges for flood management and resilience planning in a warming climate.
Keywords:
extreme rainfall
natural variability
regional climate modeling
stationary climate
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