It is widely recognized that future rainfall extremes will intensify. This expectation is tied to the Clausius-Clapeyron (CC) relation, stating that the maximum water vapour content in the atmosphere increases by 6-7% per degree warming. Scaling rates for the dependency of hourly precipitation extremes on near-surface (dew point) temperature derived from day-to-day variability have been found to exceed this relation (super-CC). However, both the applicability of this approach in a long-term climate change context, and the physical realism of super-CC rates have been questioned. Here, we analyse three different climate change experiments with a convection-permitting model over Western Europe: simple uniform-warming, 11-year pseudo-global warming and 11-year global climate model driven. The uniform-warming experiment results in consistent increases to the intensity of hourly rainfall extremes of approximately 11% per degree for moderate to high extremes. The other two, more realistic, experiments show smaller increases-usually at or below the CC rate-for moderate extremes, mostly resulting from significant decreases to rainfall occurrence. However, changes to the most extreme events are broadly consistent with 1.5-2 times the CC rate (10-14% per degree), as predicted from the present-day scaling rate for the highest percentiles. This result has important implications for climate adaptation. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'.

Data from 21 years of satellite altimeter measurements are used to identify and understand the major contributing components of sea surface height variability (SSV) on monthly time-scales in the North East Atlantic. A number of SSV drivers is considered, which are categorised into two groups; local (wind and sea surface temperature) and remote (sea level pressure and the North Atlantic oscillation index). A multiple linear regression model is constructed to model the SSV for a specific target area in the North Sea basin. Cross-correlations between candidate regressors potentially lead to ambiguity in the interpretation of the results. We therefore use an objective hierarchical selection method based on variance inflation factors to select the optimal number of regressors for the target area and accept these into the regression model if they can be associated to SSV through a direct underlying physical forcing mechanism. Results show that a region of high SSV exists off the west coast of Denmark and that it can be represented well with a regression model that uses local wind, sea surface temperature and sea level pressure as primary regressors. The regression model developed here helps to understand sea level change in the North East Atlantic. The methodology is generalised and easily applied to other regions.