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In response to flood risk, design flood estimation is a cornerstone of planning, infrastructure design, setting of insurance premiums, and emergency response planning. Under stationary assumptions, flood guidance and the methods used in design flood estimation are firmly established in practice and mature in their theoretical foundations, but under climate change, guidance is still in its infancy. Human-caused climate change is influencing factors that contribute to flood risk such as rainfall extremes and soil moisture, and there is a need for updated flood guidance. However, a barrier to updating flood guidance is the translation of the science into practical application. For example, most science pertaining to historical changes to flood risk focuses on examining trends in annual maximum flood events or the application of non-stationary flood frequency analysis. Although this science is valuable, in practice, design flood estimation focuses on exceedance probabilities much rarer than annual maximum events, such as the 1 % annual exceedance probability event or even rarer, using rainfall-based procedures, at locations where there are few to no observations of streamflow. Here, we perform a systematic review to summarize the state-of-the-art understanding of the impact of climate change on design flood estimation in the Australian context, while also drawing on international literature. In addition, a meta-analysis, whereby results from multiple studies are combined, is conducted for extreme rainfall to provide quantitative estimates of possible future changes. This information is described in the context of contemporary design flood estimation practice to facilitate the inclusion of climate science into design flood estimation practice. [ABSTRACT FROM AUTHOR]

Heatwaves have been shown to increase the likelihood and intensity of extreme rainfall occurring immediately afterward, potentially leading to increased flood risk. However, the exact mechanisms connecting heatwaves to extreme rainfall remain poorly understood. In this study, we use weather type data sets for Australia and Europe to identify weather patterns, including fronts, cyclones, and thunderstorm conditions, associated with heatwave terminations and following extreme rainfall events. We further analyze, using reanalysis data, how atmospheric instability and moisture availability change before and after the heatwave termination depending on whether the heatwave is followed by extreme rainfall, as well as the location of the heatwave. We find that most heatwaves terminate during thunderstorm and/or frontal conditions. Additionally, atmospheric instability and moisture availability increase several days before the heatwave termination; but only if heatwaves are followed by extreme rainfall. We also find that atmospheric instability and moisture after a heatwave are significantly higher than expected from climatology for the same time of the year, and that highest values of instability and moisture are associated with highest post‐heatwave rainfall intensities. We conclude that the joint presence of high atmospheric instability, moisture, as well as frontal systems are likely to explain why rainfall is generally more extreme and likely after heatwaves, as well as why this compound hazard is mainly found in the non‐arid mid and high latitudes. An improved understanding of the drivers of these compound events will help assess potential changing impacts in the future. Plain Language Summary: Extreme rainfall which can lead to flash floods is more likely to occur if it is preceded by a heatwave. The exact reasons behind this connection, however, are not fully clear. In this study we investigate the mechanistic drivers connecting heatwaves to extreme rainfall in Europe and Australia by analyzing which types of weather (e.g., fronts, cyclones, thunderstorms) are present during the transition from heatwaves to extreme rainfall. We also analyze how atmospheric characteristics associated with extreme rainfall during thunderstorms change depending on if a heatwave is followed by extreme rainfall or not. We find that heatwaves are usually followed by extreme rainfall when there are thunderstorm conditions and/or when there is the presence of a front. Further, we find that high amounts of moisture are present if heatwaves are followed by extreme rainfall and that atmospheric conditions favorable for thunderstorms, including high amounts of moisture are generally increased after heatwaves. These findings help understand how heatwaves are connected to extreme rainfall and can help assess how the risk from these events might change in the future. Key Points: The transition from heatwaves to extreme rainfall is usually associated with fronts and/or thunderstorm conditionsAtmospheric instability and moisture are larger than during non‐heatwave conditions and increase over several days before the terminationAvailability of moisture is important for producing extreme rainfall after heatwaves and explains the spatial variability for this event [ABSTRACT FROM AUTHOR]

Natural hazards often occur in combination with other natural hazards rather than as isolated events. While some combinations of hazards are well studied and their physical connection is increasingly understood, other combinations have received considerably less attention. High temperatures are known to be an important component for conditions that lead to heavy rainfall; however, sequences of heatwaves followed by heavy rainfall are not well understood, especially in a compound event context. Here, we analyse heatwave–heavy rainfall events across Australia using rainfall observations at hourly resolution. Our results show that heavy rainfall is more likely to occur if preceded by a heatwave, demonstrating that heatwave–heavy rainfall sequences should be seen as temporally compounding events. In particular, many regions in Australia experience both more frequent and more extreme wet days immediately following a heatwave. This behaviour is strongest in coastal regions, especially on the Australian east coast. These findings highlight the need for heatwave–heavy rainfall sequences to be studied as compounding events, as future changes in either hazard is likely to have impacts on related risks such as flash flooding. [ABSTRACT FROM AUTHOR]

Conventional flood risk methods typically focus on estimation at a single location, which can be inadequate for civil infrastructure systems such as road or railway infrastructure. This is because rainfall extremes are spatially dependent; to understand overall system risk, it is necessary to assess the interconnected elements of the system jointly. For example, when designing evacuation routes it is necessary to understand the risk of one part of the system failing given that another region is flooded or exceeds the level at which evacuation becomes necessary. Similarly, failure of any single part of a road section (e.g., a flooded river crossing) may lead to the wider system's failure (i.e., the entire road becomes inoperable). This study demonstrates a spatially dependent intensity–duration–frequency (IDF) framework that can be used to estimate flood risk across multiple catchments, accounting for dependence both in space and across different critical storm durations. The framework is demonstrated via a case study of a highway upgrade comprising five river crossings. The results show substantial differences in conditional and unconditional design flow estimates, highlighting the importance of taking an integrated approach. There is also a reduction in the estimated failure probability of the overall system compared with the case where each river crossing is treated independently. The results demonstrate the potential uses of spatially dependent intensity–duration–frequency methods and suggest the need for more conservative design estimates to take into account conditional risks. [ABSTRACT FROM AUTHOR]

• An inverse approach is developed to generate perturbed multi-site stochastic rain. • Captures changes in spatial pattern of rain that can impact water resource systems. • Capability is shown to accurately perturb single and multi-site rainfall features. • Streamflow is stress tested for changes in rainfall spatial correlation. With emerging evidence that anthropogenic climate change is affecting the spatial pattern of rainfall, it is important to understand how such changes affect a range of climate-sensitive systems. A method for generating multi-site rainfall sequences is presented that enables the exploration of changes in spatial rainfall patterns separately from various other potential changes (e.g. to the averages, extremes, seasonality), thereby enabling the separation of key drivers of change as part of climate stress tests. The proposed method is based on the 'inverse approach' to stochastic weather generation, and is evaluated through a case study with rainfall at 12 sites in the Barossa Valley, South Australia, using a multi-site latent variable stochastic rainfall model. The new method accurately captures (1) observed marginal attributes at each site (i.e. annual totals, seasonality, extreme rainfall, number of dry days, and intermittency), (2) spatial correlation between sites, and (3) observed attributes for areal average rainfall. We then apply the new method to stress test the response of streamflow in the region to changes in spatial rainfall patterns, quantifying how reductions in spatial correlation (while holding all other attributes constant) will lead to reduced mean annual flow and extreme flows, and increased number of low-flow days. The results show that not only can a broad range of rainfall properties (including both marginal and spatial attributes) be simulated using the inverse approach, but also that changes to the spatial properties of rainfall can have large implications on water resources systems, with the potential to amplify or dampen other changes to rainfall attributes. [ABSTRACT FROM AUTHOR]

Six weather types (WTs) are computed for tropical Australia during the wet season (November–March 1979–2015) using cluster analysis of 6-hourly low-level winds at 850 hPa. The WTs may be interpreted as a varying combination of at least five distinct phenomena operating at different time scales: the diurnal cycle, fast and recurrent atmospheric phenomena such as transient low pressure, the intraseasonal Madden–Julian oscillation, the annual cycle, and interannual variations mostly associated with El Niño–Southern Oscillation. The WTs are also strongly phase-locked onto the break/active phases of the monsoon; two WTs characterize mostly the trade-wind regime prevalent either at the start and the end of the monsoon or during its breaks, while three monsoonal WTs occur mostly during its core and active phases. The WT influence is strongest for the frequency of wet spells, while the influence on intensity varies according to the temporal aggregation of the rainfall. At hourly time scale, the climatological mean wet intensity tends to be near-constant in space and not systematically larger for the monsoonal WTs compared to other WTs. Nevertheless, one transitional WT, most prevalent around late November and characterized by weak synoptic forcings and overall drier conditions than the monsoonal WTs, is associated with an increased number of high hourly rainfall intensities for some stations, including for the interior of the Cape York Peninsula. When the temporal aggregation exceeds 6–12 h, the mean intensity tends to be larger for some of the monsoonal WTs, in association with more frequent and also slightly longer wet spells. [ABSTRACT FROM AUTHOR]

Summary Scenario-neutral approaches are being used increasingly for climate impact assessments, as they allow water resource system performance to be evaluated independently of climate change projections. An important element of these approaches is the generation of perturbed series of hydrometeorological variables that form the inputs to hydrologic and water resource assessment models, with most scenario-neutral studies to-date considering only shifts in the average and a limited number of other statistics of each climate variable. In this study, a stochastic generation approach is used to perturb not only the average of the relevant hydrometeorological variables, but also attributes such as the intermittency and extremes. An optimization-based inverse approach is developed to obtain hydrometeorological time series with uniform coverage across the possible ranges of rainfall attributes (referred to as the ‘exposure space’). The approach is demonstrated on a widely used rainfall generator, WGEN, for a case study at Adelaide, Australia, and is shown to be capable of producing evenly-distributed samples over the exposure space. The inverse approach expands the applicability of the scenario-neutral approach in evaluating a water resource system’s sensitivity to a wider range of plausible climate change scenarios. [ABSTRACT FROM AUTHOR]

Historical in situ sub-daily rainfall observations are essential for the understanding of short-duration rainfall extremes but records are typically not readily accessible and data are often subject to errors and inhomogeneities. Furthermore, these events are poorly quantified in projections of future climate change making adaptation to the risk of flash flooding problematic. Consequently, knowledge of the processes contributing to intense, short-duration rainfall is less complete compared with those on daily timescales. The INTENSE project is addressing this global challenge by undertaking a data collection initiative that is coupled with advances in high-resolution climate modelling to better understand key processes and likely future change. The project has so far acquired data from over 23 000 rain gauges for its global sub-daily rainfall dataset (GSDR) and has provided evidence of an intensification of hourly extremes over the US. Studies of these observations, combined with model simulations, will continue to advance our understanding of the role of local-scale thermodynamics and large-scale atmospheric circulation in the generation of these events and how these might change in the future. [ABSTRACT FROM AUTHOR]

Scenario-neutral approaches are being used increasingly for assessing the potential impact of climate change on water resource systems, as these approaches allow the performance of these systems to be evaluated independently of climate change projections. However, practical implementations of these approaches are still scarce, with a key limitation being the difficulty of generating a range of plausible future time series of hydro-meteorological data. In this study we apply a recently developed inverse stochastic generation approach to support the scenario-neutral analysis, and thus identify the key hydro-meteorological variables to which the system is most sensitive. The stochastic generator simulates synthetic hydro-meteorological time series that represent plausible future changes in (1) the average, extremes and seasonal patterns of rainfall; and (2) the average values of temperature ( T a ), relative humidity ( RH ) and wind speed ( u z ) as variables that drive PET. These hydro-meteorological time series are then fed through a conceptual rainfall-runoff model to simulate the potential changes in runoff as a function of changes in the hydro-meteorological variables, and runoff sensitivity is assessed with both correlation and Sobol’ sensitivity analyses. The method was applied to a case study catchment in South Australia, and the results showed that the most important hydro-meteorological attributes for runoff were winter rainfall followed by the annual average rainfall, while the PET-related meteorological variables had comparatively little impact. The high importance of winter rainfall can be related to the winter-dominated nature of both the rainfall and runoff regimes in this catchment. The approach illustrated in this study can greatly enhance our understanding of the key hydro-meteorological attributes and processes that are likely to drive catchment runoff under a changing climate, thus enabling the design of tailored climate impact assessments to specific water resource systems. [ABSTRACT FROM AUTHOR]

Sub-daily rainfall extremes are of significant societal interest, with implications for flash flooding and the design of urban stormwater systems. It is increasingly recognised that extreme subdaily rainfall will intensify as a result of global temperature increases, with regional climate models (RCMs) representing one of the principal lines of evidence on the likely magnitude and spatiotemporal characteristics of these changes. To evaluate the ability of RCMs to simulate subdaily extremes, it is common to compare the simulated statistical characteristics of the extreme rainfall events with those from observational records. While such analyses are important, they provide insufficient insight into whether the RCM reproduces the correct underlying physical processes; in other words, whether the model 'gets the right answers for the right reasons'. This paper develops a range of metrics to assess the performance of RCMs in capturing the physical mechanisms that produce extreme rainfall. These metrics include the diurnal and seasonal cycles, relationship between rainfall intensity and temperature, temporal scaling, and the spatial structure of extreme rainfall events. We evaluate a high resolution RCM-the Weather Research Forecasting model-over the Greater Sydney region, using three alternative parametrization schemes. The model shows consistency with the observations for most of the proposed metrics. Where differences exist, these are dependent on both the rainfall duration and model parameterization strategy. The use of physically meaningful performance metrics not only enhances the confidence in model simulations, but also provides better diagnostic power to assist with future model improvement. [ABSTRACT FROM AUTHOR]

Spatial statistical methods are often employed to improve precision when estimating marginal distributions of extreme rainfall. Methods such as max-stable and copula models parameterize the spatial dependence and provide a continuous spatial representation. Alternatively, the independence method can be used to estimate marginal parameters without the need for parameterizing the spatial dependence, and this method has been under-utilized in hydrologic applications. This paper investigates the effectiveness of the independence method for marginal parameter estimation of spatially dependent extremes. Its performance is compared with three spatial dependence models (max-stable Brown-Resnick, max-stable Schlather, and Gaussian copula) by means of a simulation study. The independence method is statistically robust in estimating parameters and their associated confidence intervals for spatial extremes with various underlying dependence structures. The spatial dependence models perform comparably with the independence method when the spatial dependence structure is correctly specified; otherwise they exhibit considerably worse performance. We conclude that the independence method is more appealing for modeling the marginal distributions of spatial extremes (e.g., regional estimation of trends in rainfall extremes) due to its greater robustness and simplicity. The four statistical methods are illustrated using a spatial data set comprising 69 subdaily rainfall series from the Greater Sydney region, Australia. [ABSTRACT FROM AUTHOR]

Accounting for dependence between extreme rainfall and storm surge can be critical for correctly estimating coastal flood risk. Several statistical methods are available for modeling such extremal dependence, but the comparative performance of these methods for quantifying the exceedance probability of rare coastal floods is unknown. This paper compares three classes of statistical methods-threshold-excess, point process, and conditional-in terms of their ability to quantify flood risk. The threshold-excess method offers approximately unbiased estimates for dependence parameters, but its application for quantifying flood risk is limited because it is unable to handle situations where only one of the two variables is extreme. In contrast, the point process method (with the logistic and negative logistic models) and the conditional method describe the full distribution of extremes, but they overestimate and underestimate the dependence strength, respectively. We conclude that the point process method is the most suitable approach for modeling dependence between extreme rainfall and storm surge when the dependence is relatively strong, while none of the three methods produces satisfactory results for bivariate extremes with very weak dependence. It is therefore important to take the bias of each method into account when applying them to flood estimation problems. A case study is used to demonstrate the three statistical methods and illustrate the implication of dependence to flood risk. [ABSTRACT FROM AUTHOR]

Climate and weather variables such as rainfall, temperature, and pressure are indicators for hazards such as tropical cyclones, floods, and fires. The impact of these events can be due to a single variable being in an extreme state, but more often it is the result of a combination of variables not all of which are necessarily extreme. Here, the combination of variables or events that lead to an extreme impact is referred to as a compound event. Any given compound event will depend upon the nature and number of physical variables, the range of spatial and temporal scales, the strength of dependence between processes, and the perspective of the stakeholder who defines the impact. Modeling compound events is a large, complex, and interdisciplinary undertaking. To facilitate this task we propose the use of influence diagrams for defining, mapping, analyzing, modeling, and communicating the risk of the compound event. Ultimately, a greater appreciation of compound events will lead to further insight and a changed perspective on how impact risks are associated with climate-related hazards. WIREs Clim Change 2014, 5:113-128. doi: 10.1002/wcc.252 Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. [ABSTRACT FROM AUTHOR]

Highlights: [•] Dependence between rainfall and surge is statistically significant. [•] Majority of the tide gauges show asymptotic dependence. [•] Spatial and temporal variability of dependence was observed. [•] The obtained results have significant implications on flood risk estimation. [Copyright &y& Elsevier]

This study investigates the presence of trends in annual maximum daily precipitation time series obtained from a global dataset of 8326 high-quality land-based observing stations with more than 30 years of record over the period from 1900 to 2009. Two complementary statistical techniques were adopted to evaluate the possible nonstationary behavior of these precipitation data. The first was a Mann-Kendall nonparametric trend test, and it was used to evaluate the existence of monotonic trends. The second was a nonstationary generalized extreme value analysis, and it was used to determine the strength of association between the precipitation extremes and globally averaged near-surface temperature. The outcomes are that statistically significant increasing trends can be detected at the global scale, with close to two-thirds of stations showing increases. Furthermore, there is a statistically significant association with globally averaged near-surface temperature, with the median intensity of extreme precipitation changing in proportion with changes in global mean temperature at a rate of between 5.9% and 7.7% K−1, depending on the method of analysis. This ratio was robust irrespective of record length or time period considered and was not strongly biased by the uneven global coverage of precipitation data. Finally, there is a distinct meridional variation, with the greatest sensitivity occurring in the tropics and higher latitudes and the minima around 13°S and 11°N. The greatest uncertainty was near the equator because of the limited number of sufficiently long precipitation records, and there remains an urgent need to improve data collection in this region to better constrain future changes in tropical precipitation. [ABSTRACT FROM AUTHOR]

Summary: This paper describes an algorithm for disaggregating daily rainfall into sub-daily rainfall ‘fragments’ (fine-resolution rainfall sequences) under a future, warmer climate. The algorithm uses a combined generalised additive model (GAM) and method of fragments (MoFs) framework to resample sub-daily rainfall fragments from the historical record conditional on daily rainfall amount and a range of atmospheric covariates. The rationale is that as the atmosphere warms, future rainfall patterns will be more reflective of historical rainfall patterns corresponding to warmer days at the same location, or to locations which have an atmospheric profile more reflective of expected future climate. It was found that the daily to sub-daily scaling relationship varied significantly by season and by location, with rainfall patterns on warmer seasons or at warmer locations typically showing more intense rainfall occurring over shorter periods compared with cooler seasons and stations. Importantly, by regressing against atmospheric covariates such as temperature, this effect was substantially reduced, suggesting that the approach may also be valid when extrapolating to a future climate. The GAM–MoF algorithm was then applied to nine stations around Australia, with the results showing that relative to the daily rainfall amount, the maximum intensity of short duration rainfall increased by between 4.1% and 13.4% per degree change in temperature for the maximum six minute burst, and by between 3.1% and 6.8% for the maximum 1h burst. The fraction of each wet day with no rainfall also increased by between 1.5% and 3.5%. This highlights that a significant proportion of the change to the distribution of rainfall is likely to occur at sub-daily timescales, with important implications for many hydrological systems. [Copyright &y& Elsevier]

The relationship between seasonal aggregate rainfall and large-scale climate modes, particularly the El Niño-Southern Oscillation (ENSO), has been the subject of a significant and ongoing research effort. However, relatively little is known about how the character of individual rainfall events varies as a function of each of these climate modes. This study investigates the change in rainfall occurrence, intensity, and storm interevent time at both daily and subdaily time scales in east Australia, as a function of indices for ENSO, the Indian Ocean dipole (IOD), and the southern annular mode (SAM), with a focus on the cool season months. Long-record datasets have been used to sample a large variety of climate events for better statistical significance. Results using both the daily and subdaily rainfall datasets consistently show that it is the occurrence of rainfall events, rather than the average intensity of rainfall during the events, which is most strongly influenced by each of the climate modes. This is shown to be most likely associated with changes to the time between wet spells. Furthermore, it is found that despite the recent attention in the research literature on other climate modes, ENSO remains the leading driver of rainfall variability over east Australia, particularly farther inland during the winter and spring seasons. These results have important implications for how water resources are managed, as well as how the implications of large-scale climate modes are included in rainfall models to best capture interannual and longer-scale variability. [ABSTRACT FROM AUTHOR]

This paper is the second of two in the current issue that presents a framework for simulating continuous (uninterrupted) rainfall sequences at both gaged and ungaged locations. The ultimate objective of the papers is to present a methodology for stochastically generating continuous subdaily rainfall sequences at any location such that the statistics at a range of aggregation scales are preserved. In this paper we complete the regionalized algorithm by adopting a rationale for generating daily sequences at any location by sampling daily rainfall records from "nearby" gages with statistically similar rainfall sequences.The approach consists of two distinct steps: first the identification of a set of locations with daily rainfall sequences that are statistically similar to the location of interest, and second the development of an algorithm to sample daily rainfall from those locations. In the first step, the similarity between all bivariate combinations of 2708 daily rainfall records across Australia were considered, and a logistic regression model was formulated to predict the similarity between stations as a function of a number of physiographic covariates. Based on the model results, a number of nearby locations with adequate daily rainfall records are identified for any ungaged location of interest (the "target" location), and then used as the basis for stochastically generating the daily rainfall sequences. The continuous simulation algorithm was tested at five locations where long historical daily rainfall records are available for comparison, and found to perform well in representing the distributional and dependence attributes of the observed daily record. These daily sequences were then used to disaggregate to a subdaily time step using the rainfall state-based disaggregation approach described in the first paper, and found to provide a good representation of the continuous rainfall sequences at the location of interest. [ABSTRACT FROM AUTHOR]

This paper is the first of two in the current issue that presents a framework for generating continuous (uninterrupted) rainfall sequences at both gaged and ungaged point locations. The ultimate objective is to present a methodology for stochastically generating continuous subdaily rainfall sequences at any location such that the statistics at a range of aggregation scales are preserved. This first paper presents a regionalized nonparametric daily disaggregation model in which, conditional on a daily rainfall amount and previous- and next-day wetness states at the location of interest, subdaily fragments are resampled using continuous records at nearby locations. The second paper then focuses on a regionalized daily rainfall generation model.To enable the substitution of subdaily rainfall at nearby locations for subdaily rainfall at the location of interest, it is necessary to identify locations with "similar" daily to subdaily scaling characteristics. We use a two-sample, two-dimensional Kolmogorov-Smirnov (K-S) test to identify whether the daily to subdaily scaling relationships are statistically similar between all possible station pairs sampled from 232 gages located throughout Australia. This step is followed by a logistic regression to determine the influence of the covariates of latitude, longitude, elevation, and distance to the coast on the probability that the scaling at any two locations will be similar. The model is tested at five locations, where recorded subdaily data was available for comparison, and results indicate good model performance, particularly in preserving the probability distribution of extremes and the antecedent rainfall prior to the storm event. [ABSTRACT FROM AUTHOR]

Summary: Non-stationarity in extreme precipitation at sub-daily and daily timescales is assessed using a spatial extreme value model based on max-stable process theory. This approach, which was developed to simulate spatial fields comprising observations from multiple point locations, significantly increases the precision of a statistical inference compared to standard univariate methods. Applying the technique to a field of annual maxima derived from 30 sub-daily gauges in east Australia from 1965 to 2005, we find a statistically significant increase of 18% for 6-min rainfall over this period, with smaller increases for longer duration events. We also find an increase of 5.6% and 22.5% per degree of Australian land surface temperature and global sea surface temperature at 6-min durations, respectively, again with smaller scaling relationships for longer durations. In contrast, limited change could be observed in daily rainfall at most locations, with the exception of a statistically significant decline of 7.4% per degree land surface temperature in southwest Western Australia. These results suggest both the importance of better understanding changes to precipitation at the sub-daily timescale, as well as the need to more precisely simulate temporal variability by accounting for the spatial nature of precipitation in the statistical model. [ABSTRACT FROM AUTHOR]

Climate change impact studies for water resource applications, such as the development of projections of reservoir yields or the assessment of likely frequency and amplitude of drought under a future climate, require that the year-to-year persistence in a range of hydrological variables such as catchment average rainfall be properly represented. This persistence is often attributable to low-frequency variability in the global sea surface temperature (SST) field and other large-scale climate variables through a complex sequence of teleconnections. To evaluate the capacity of general circulation models (GCMs) to accurately represent this low-frequency variability, a set of wavelet-based skill measures has been developed to compare GCM performance in representing interannual variability with the observed global SST data, as well as to assess the extent to which this variability is imparted in precipitation and surface pressure anomaly fields. A validation of the derived skill measures is performed using GCM precipitation as an input in a reservoir storage context, with the accuracy of reservoir storage estimates shown to be improved by using GCM outputs that correctly represent the observed low-frequency variability. Significant differences in the performance of different GCMs is demonstrated, suggesting that judicious selection of models is required if the climate impact assessment is sensitive to low-frequency variability. The two GCMs that were found to exhibit the most appropriate representation of global low-frequency variability for individual variables assessed were the Istituto Nazionale di Geofisica e Vulcanologia (INGV) ECHAM4 and L''Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4); when considering all three variables, the Max Planck Institute (MPI) ECHAM5 performed well. Importantly, models that represented interannual variability well for SST also performed well for the other two variables, while models that performed poorly for SST also had consistently low skill across the remaining variables. [ABSTRACT FROM AUTHOR]

Highlights • Areal reduction factors (ARFs) are needed to estimate flood risk for rare events. • Behaviour of ARFs for long return periods depends on asymptotic properties. • Spatial rainfall is asymptotically independent. • Areal reduction factors decrease with increasing return period. Abstract Areal reduction factors (ARFs) transform an estimate of extreme rainfall at a point to an estimate of extreme rainfall over a spatial domain, and are commonly used in flood risk estimation. For applications such as the design of large infrastructure, dam safety and land use planning, ARFs are needed to estimate flood risk for very rare events that are often larger than the biggest historical events. The nature of the relationship between ARFs and frequency for long return periods is unclear as it depends on the asymptotic dependence structure of rainfall over a region, i.e., the extent to which rainfall from a surrounding region is extreme as rainfall at a point becomes more extreme. Miscalculating this for very rare events could lead to poor design of infrastructure. To investigate this, spatial rainfall processes are simulated using asymptotically dependent and independent models, and the implications for ARFs of the asymptotic assumptions are explored in a synthetic study. The models are then applied to a case study in Victoria, Australia, using 88 daily rainfall gauges with 50 years of data. The analysis shows that the observed data follow the behaviour of an asymptotically independent process, leading to ARFs that decrease with increasing return period. The study demonstrates that the use of inverted max-stable process models to simulate ARFs can provide a rigorous alternative to empirical approaches, particularly for long return periods requiring significant extrapolation from the data. [ABSTRACT FROM AUTHOR]