Evidence that extreme rainfall intensity is increasing at the global scale has strengthened considerably in recent years. Research now indicates that the greatest increases are likely to occur in short-duration storms lasting less than a day, potentially leading to an increase in the magnitude and frequency of flash floods. This review examines the evidence for subdaily extreme rainfall intensification due to anthropogenic climate change and describes our current physical understanding of the association between subdaily extreme rainfall intensity and atmospheric temperature. We also examine the nature, quality, and quantity of information needed to allow society to adapt successfully to predicted future changes, and discuss the roles of observational and modeling studies in helping us to better understand the physical processes that can influence subdaily extreme rainfall characteristics. We conclude by describing the types of research required to produce a more thorough understanding of the relationships between local-scale thermodynamic effects, large-scale atmospheric circulation, and subdaily extreme rainfall intensity.

[1] Climate change impact assessments of water resources systems require simulations of precipitation and evaporation that exhibit distributional and persistence attributes similar to the historical record. Specifically, there is a need to ensure general circulation model (GCM) simulations of rainfall for the current climate exhibit low-frequency variability that is consistent with observed data. Inability to represent low-frequency variability in precipitation and flow leads to biased estimates of the security offered by water resources systems in a warmer climate. This paper presents a method to postprocess GCM precipitation simulations by imparting correct distributional and persistence attributes, resulting in sequences that are representative of observed records across a range of time scales. The proposed approach is named nesting bias correction (NBC), the rationale being to correct distributional and persistence bias from fine to progressively longer time scales. In the results presented here, distributional attributes have been represented by order 1 and 2 moments with persistence represented by lag 1 autocorrelation coefficients at monthly and annual time scales. The NBC method was applied to the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mk3.5 and MIROC 3.2 hires rainfall simulations for Australia. It was found that the nesting method worked well to correct means, standard deviations, and lag 1 autocorrelations when the biases in the raw GCM outputs were not too large. While the bias correction improves the representation of distributional and persistence attributes at the time scales considered, there is room for representation of longer-term persistence by extending to time scales longer than a year.

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Simulations from general circulation models are now being used for a variety of studies and purposes. With up to 23 different GCMs now available, it is desirable to determine whether a specific variable from a particular model is representative of the ensemble mean, which is often assumed to indicate the likely state of that variable in the future. The answers are important for decision makers and researchers using selective model outputs for follow-on studies such as statistical downscaling, which currently assume all model outputs are simulated with equal reliability.A skill score, termed the variable convergence score (VCS), has been derived that can be used to rank variables based on the coefficient of variation of the ensemble. The key benefit is the development of a simple methodology that allows for a quantitative assessment between different hydroclimatic variables.The VCS methodology has been applied to the outputs of nine GCMs for eight different variables and two emission scenarios to provide a relative ranking of the variables averaged across Australia and over different climatic regions of the country. The methodology, however, would be applicable for any region or any variable of interest from GCMs.It was found that the surface variables with the highest scores are pressure, temperature, and humidity. Regionally in Australia, models again show the best agreement in the surface pressure projections. The tropical and southwestern temperate zones show the overall highest variable convergence when all variables are considered. The desert zone shows relatively low model agreement, particularly in the projections of precipitation and specific humidity.

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