Spatially and temporally consistent prediction of heavy precipitation from mean values

Abstract

The discovery of an apparently universal function describing the frequency distribution for 24-h precipitation leads to a formula relating heavy precipitation to the mean amounts and the number of days when it rains. The formula has been validated using more than 30,000 daily rain-gauge records from around the world. Extreme precipitation can cause flooding, result in substantial damages and have detrimental effects on ecosystems1,2. Climate adaptation must therefore account for the greatest precipitation amounts that may be expected over a certain time span3. The recurrence of extreme-to-heavy precipitation is notoriously hard to predict, yet cost–benefit estimates of mitigation and successful climate adaptation will need reliable information about percentiles for daily precipitation. Here we present a new and simple formula that relates wet-day mean precipitation to heavy precipitation, providing a method for predicting and downscaling daily precipitation statistics. We examined 32,857 daily rain-gauge records from around the world and the evaluation of the method demonstrated that wet-day precipitation percentiles can be predicted with high accuracy. Evaluations against independent data demonstrated high skill in both space and time, indicating a highly robust methodology.

Cite this article

Benestad, R., Nychka, D. & Mearns, L. Spatially and temporally consistent prediction of heavy precipitation from mean values. Nature Clim Change 2, 544–547 (2012). https://doi.org/10.1038/nclimate1497

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