Constraining extreme precipitation projections using past precipitation variability

Wenxia Zhang, Kalli Furtado, Tianjun Zhou (), Peili Wu and Xiaolong Chen
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Wenxia Zhang: Institute of Atmospheric Physics, Chinese Academy of Sciences
Kalli Furtado: Met Office
Tianjun Zhou: Institute of Atmospheric Physics, Chinese Academy of Sciences
Peili Wu: Met Office
Xiaolong Chen: Institute of Atmospheric Physics, Chinese Academy of Sciences

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract Projected changes of future precipitation extremes exhibit substantial uncertainties among climate models, posing grand challenges to climate actions and adaptation planning. Practical methods for narrowing the projection uncertainty remain elusive. Here, using large model ensembles, we show that the uncertainty in projections of future extratropical extreme precipitation is significantly correlated with the model representations of present-day precipitation variability. Models with weaker present-day precipitation variability tend to project larger increases in extreme precipitation occurrences under a given global warming increment. This relationship can be explained statistically using idealized distributions for precipitation. This emergent relationship provides a powerful constraint on future projections of extreme precipitation from observed present-day precipitation variability, which reduces projection uncertainty by 20–40% over extratropical regions. Because of the widespread impacts of extreme precipitation, this has not only provided useful insights into understanding uncertainties in current model projections, but is also expected to bring potential socio-economic benefits in climate change adaptation planning.

Date: 2022
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DOI: 10.1038/s41467-022-34006-0

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