A derailment on 2020-08-12 near Carmont, Scotland resulted in the death of three people and the injury of the other 6 people on the train. The cause of the derailment was gravel washed onto the tracks due to heavy rain and an improperly built drain. The heavy rainfall near the derailment site lasted about 4 h, with a large burst of rain just prior to the derailment. The rainfall event was heavy with return periods between 1-in-20 and 1-in-40 summers. Simple theory suggests that extreme precipitation should increase with changes in saturated humidity (Clausius–Clapeyron; CC). The UK’s convective permitting model suggests, that extreme hourly rainfall near Carmont changes at about 1.5× CC rates while extreme four hourly rates increases below CC rates. This leads to an increased probability, relative to late 19th century conditions, of hourly (4 h) extremes similar to that which occurred near the derailment by about 20 (15)% with a further frequency increase of about 20 (15)% for such events in a +2C warmer world. Some characteristics of the simulated extreme rain evaluate well against radar values, but the simulated rainfall is about 20%–30% larger than the radar rainfall. This likely reflects shortcomings in radar calibration of extreme rainfall though poor model performance is also possible.

The UK recorded its first exceedance of 40°C in July 2022. To assess the current chance of recurrence, we use a large climate model ensemble approach and find the 2023 return period to be 1‐in‐24 years (1/20–1/29 years confidence interval). However, the likelihood of 40°C has been accelerating and we estimate a 50–50 chance of another exceedance in the next 12 years, 4 years earlier than in a static 2023 climate. Far higher temperatures – over 45°C – and more sustained heatwaves may be possible today. Improved understanding of baseline exposure to temperature extremes could inform strategic heatwave preparation.

1976 was one of the most acute droughts in the UK, exceptional due to the compounding effects of low rainfall and hot summer temperatures. In this study, we provide a multiperspective view of the likelihood of a 1976-like compound event occurring now and into the future. We find a high level of consistency in the messages emerging across a range of different approaches and climate modelling tools, from convection-permitting climate projections to decadal hindcasts and global coupled-model attribution ensembles. 1976 summer average temperatures remain, at the time of writing, amongst the highest on record, but with warming are becoming increasingly common. The nine-month rainfall deficit to August 1976 was incredibly rare. Analysis here indicates that compound extremes like 1976 are expected to occur on time-scales of hundreds to thousands of years in the present-day climate, decreasing slightly into the future. The probability remains very small even if we account for favourable sea surface temperatures and atmospheric circulation that occurred in 1976. Similar but less severe events with a 1% chance of occurring in the present day are five times more frequent from the 2040s under RCP8.5. The occurrence of such compound events is significantly (up to an order of magnitude) higher than expected if temperature and rainfall extremes occurred independently. In general, differences in likelihood estimates between approaches can begin to be understood from how dependence between variables is handled, differences in bias correction, and different levels of conditioning (i.e., the probability given particular atmospheric or ocean states). The appropriate choice of conditioning very much depends on the question being asked and its unconscious use may lead to apparent contradictions. Parallels can be drawn between 1976 and the recent summer of 2022, and results here suggest that with hotter summers we should be prepared for more severe droughts like 1976 in the future.

Although most models for rainfall extremes focus on point-wise values, it is aggregated precipitation over areas up to river catchment scale that is of the most interest. To capture the joint behaviour of precipitation aggregates evaluated at different spatial scales, parsimonious and effective models must be built with knowledge of the underlying spatial process. Precipitation is driven by a mixture of processes acting at different scales and intensities, e.g., convective and frontal, with extremes of aggregates for typical catchment sizes arising from extremes of only one of these processes, rather than a combination of them. High-intensity convective events cause extreme spatial aggregates at small scales but the contribution of lower-intensity large-scale fronts is likely to increase as the area aggregated increases. Thus, to capture small to large scale spatial aggregates within a single approach requires a model that can accurately capture the extremal properties of both convective and frontal events. Previous extreme value methods have ignored this mixture structure; we propose a spatial extreme value model which is a mixture of two components with different marginal and dependence models that are able to capture the extremal behaviour of convective and frontal rainfall and more faithfully reproduces spatial aggregates for a wide range of scales. Modelling extremes of the frontal component raises new challenges due to it exhibiting strong long-range extremal spatial dependence. Our modelling approach is applied to fine-scale, high-dimensional, gridded precipitation data. We show that accounting for the mixture structure improves the joint inference on extremes of spatial aggregates over regions of different sizes.

PRIMAVERA was a European Union Horizon 2020 project whose primary aim was to generate advanced and well-evaluated high-resolution global climate model datasets, for the benefit of governments, business and society in general. Following consultation with members of the insurance industry, we have used a PRIMAVERA multi-model ensemble to generate a European winter windstorm event set for use in insurance risk analysis, containing approximately 1300 years of windstorm data. The uploaded dataset contains the model and re-analysis windstorm footprints in netcdf format and documentation of the data. Further information is given in Lockwood et al., Using high-resolution global climate models from the PRIMAVERA project to create a European winter windstorm event set, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2022-12, in review, 2022. Any products or applications which use this dataset must state the following attribution “Acknowledgment to the PRIMAVERA partners. The information/material contained has been produced with funding from the European Union’s Horizon 2020 Research & Innovation Programme under grant agreement no. 641727.”

The UNSEEN (unprecedented simulated extremes using ensemble) method involves using a large ensemble of climate model simulations to increase the sample size of rare events. Here we extend UNSEEN to focus on intense summertime daily rainfall, estimating plausible rainfall extremes in the current climate. To address modelling limitations simulations from two climate models were used; an initialised 25 km global model that uses parameterised convection, and a dynamically downscaled 2.2 km model that uses explicit convection. In terms of the statistical characteristics that govern very rare return periods, the models are not significantly different from the observations across much of the UK. Our analysis provides more precise estimates of 1000 year return levels for extreme daily rainfall, reducing sampling uncertainty by 70%–90% compared to using observations alone. This framework enables observed daily storm profiles to be adjusted to more statistically robust estimates of extreme rainfall. For a damaging storm in July 2007 which led to surface water flooding, we estimate physically plausible increases in the total daily rainfall of 50%–100%. For much of the UK the annual chance of record-breaking daily summertime rainfall is estimated to be around 1% per year in the present-day climate. Analysis of the dynamical states in our UNSEEN events indicates that heavy daily rainfall is associated with a southward displaced and meandering North Atlantic jet stream, increasing the advection of warm moist air from across Southern Europe and the Mediterranean, and intensifying extratropical storms. This work represents an advancement in the use of climate modelling for estimating present-day climate hazards and outlines a framework for applying UNSEEN at higher spatial and temporal resolutions.

PRIMAVERA (process-based climate simulation: advances in high-resolution modelling and European climate risk assessments) was a European Union Horizon 2020 project whose primary aim was to generate advanced and well-evaluated high-resolution global climate model datasets for the benefit of governments, business and society in general. Following consultation with members of the insurance industry, we have used a PRIMAVERA multi-model ensemble to generate a European winter windstorm event set for use in insurance risk analysis, containing approximately 1300 years of windstorm data. The data are available at https://doi.org/10.5281/zenodo.6492182. To create the storm footprints for the event set, the storms in the PRIMAVERA models are identified through tracking. A method is developed to separate the winds from storms occurring in the domain at the same time. The wind footprints are bias corrected and converted to 3 s gusts onto a uniform grid using quantile mapping. The distribution of the number of model storms per season as a function of estimated loss is consistent with re-analysis, as are the total losses per season, and the additional event set data greatly reduce uncertainty on return period magnitudes. The event set also reproduces the temporally clustered nature of European windstorms. Since the event set is generated from global climate models, it can help to quantify the non-linear relationship between large-scale climate indices such as the North Atlantic Oscillation (NAO) and windstorm damage. Although we find only a moderate positive correlation between extended winter NAO and storm damage in northern European countries (consistent with re-analysis), there is a large change in risk of extreme seasons between negative and positive NAO states. The intensities of the most severe storms in the event set are, however, sensitive to the gust conversion and bias correction method used, so care should be taken when interpreting the expected damages for very long return periods.

Inference on the extremal behaviour of spatial aggregates of precipitation is important for quantifying river flood risk. There are two classes of previous approach, with one failing to ensure self-consistency in inference across different regions of aggregation and the other imposing highly restrictive assumptions. To overcome these issues, we propose a model for high-resolution precipitation data, from which we can simulate realistic fields and explore the behaviour of spatial aggregates. Recent developments have seen spatial extensions of the Heffernan and Tawn (2004) model for conditional multivariate extremes, which can handle a wide range of dependence structures. Our contribution is twofold: extensions and improvements of this approach and its model inference for high-dimensional data; and a novel framework for deriving aggregates addressing edge effects and sub-regions without rain. We apply our modelling approach to gridded East-Anglia, UK precipitation data. Return-level curves for spatial aggregates over different regions of various sizes are estimated and shown to fit very well to the data.

The UNSEEN (UNprecedented Simulated Extremes using ENsembles) method involves using a large ensemble of initialised climate model simulations to increase the sample size of rare events. In this work we extended UNSEEN to focus on intense summertime daily rainfall events. Specifically, plausible extreme rainfall scenarios were developed to help understand potential surface water flooding impacts, and ultimately better inform flood management and resilience across the UK. To help address modelling limitations a large ensemble of simulations from two climate models were used; an initialised 25km global model that uses parametrized convection, and a dynamically downscaled 2.2km model that uses explicit convection. Climate model fidelity was assessed using a regional pooling technique based on extreme value theory. Across much of the UK both models are indistinguishable from the observations in terms of the statistical characteristics which govern the magnitude of very rare return periods. The UNSEEN analysis provides new estimates of plausible extreme return levels (i.e. 1-in-1000 year) across the UK and can reduce uncertainty in the expected frequency of very rare events by 50-70% compared to estimates using observations alone. These results enable suitable observed rainfall profiles to be uplifted to plausible extreme return levels, which can then be used within regional hydrological models to stress test surface flooding scenarios. The annual chance of unprecedented daily rainfall events in the current climate is also quantified, and found to be up to 5% (1-in-20 year return level) for many grid cells across southern parts of the UK. Finally, a significant benefit of the UNSEEN approach over purely statistical emulators is the use of dynamical climate models which allow the large-scale dynamical drivers of extreme daily summertime rainfall to be assessed.

Fluvial flooding is not caused by high intensity rainfall at a single location, rather it is caused by the extremes of precipitation events aggregated over spatial catchment areas. Accurate modelling of the tail behaviour of such events can help to mitigate the financial aspects associated with floods, especially if river defences are built within specification to withstand an n-year event of this kind. Within an extreme value analysis framework, univariate methods for estimating the size of these n-year events are well studied and cemented in asymptotic theory.To complement these techniques, we develop a high-resolution spatial model for extreme precipitation by providing a fully spatial extension of the conditional approach for modelling multivariate extremes. We simulate realistic precipitation fields from this model and use univariate techniques to make inference about the extremal behaviour of aggregates over specified spatial domains. The challenge of zero precipitation data is overcome and further applications of the model are discussed. The model is fit to data from a convection permitting forecast model within the 2018 UK Climate Projections (UKCP18).

Richards and Tawn gratefully acknowledge funding through the STOR-i Doctoral Training Centre and Engineering and Physical Sciences Research Council (grant EP/L015692/1). This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-CRG2020-4394. Brown was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra.

A novel approach to quantify the present and future heatwave hazard is presented which can discern characteristics beyond what can be achieved from current approaches, such as distributions of severity, duration and frequency, including very low probability events that may not have been seen. A statistical model is built that represents the seasonal cycle, climate change, magnitude and temporal behaviour of all temperatures above a moderately high time varying threshold at a site from which very large samples of temperature time series can be drawn. From these, user defined heatwaves can be extracted and precise empirical statistics calculated, allowing application to a wide range of problems such as heatwave impact on the pollination of food crops. Fitting the heatwave model to climate model simulations allows the changing severity, duration and frequency of heatwaves from the past to the future to be quantified. This approach is validated by reproducing the heatwave climatology of a pre-industrial 3 500 year GCM control run from a 110 year future emission run of the same GCM. Using this methodology, future heatwave changes for the most populous cities from 20 countries are derived from a 44 member ensemble of 28 GCMs from the CMIP5 archive forced with the RCP 8.5 emission scenario. Compared to 2006, absolute temperatures of 4–10 day long heatwaves are projected to be between 3.4 to 6.6 °C hotter in 2099, though the magnitude of the increases are independent of heatwave rarity and duration. For 13 of the cities no significant future changes in the distribution of durations are found relative to a contemporaneous threshold. Six cities show future heatwaves will have a tendency to be longer and one to be shorter. Half of the cities show increases in severity, the time integrated temperature anomalies above the contemporaneous threshold. The largest changes are found for Paris with 100 year return level severity changes equivalent to mean heatwave temperatures increasing by 3.4 °C or by 1.7 °C for 5 and 10 day heatwaves respectively, and the rate of 10 day heatwaves increasing by 130%.

Windstorms are a primary natural hazard affecting Europe that are commonly linked to substantial property and infrastructural damage and are responsible for the largest spatially aggregated financial losses. Such extreme winds are typically generated by extratropical cyclone systems originating in the North Atlantic and passing over Europe. Previous statistical studies tend to model extreme winds at a given set of sites, corresponding to inference in an Eulerian framework. Such inference cannot incorporate knowledge of the life cycle and progression of extratropical cyclones across the region and is forced to make restrictive assumptions about the extremal dependence structure. We take an entirely different approach which overcomes these limitations by working in a Lagrangian framework. Specifically, we model the development of windstorms over time, preserving the physical characteristics linking the windstorm and the cyclone track, the path of local vorticity maxima, and make a key finding that the spatial extent of extratropical windstorms becomes more localized as its magnitude increases irrespective of the location of the storm track. Our model allows simulation of synthetic windstorm events to derive the joint distributional features over any set of sites giving physically consistent extrapolations to rarer events. From such simulations improved estimates of this hazard can be achieved in terms of both intensity and area affected.

Windstorms are a primary natural hazard affecting Europe that are commonly linked to substantial property and infrastructural damage and are responsible for the largest spatially aggregated financial losses. Such extreme winds are typically generated by extratropical cyclone systems originating in the North Atlantic and passing over Europe. Previous statistical studies tend to model extreme winds at a given set of sites, corresponding to inference in an Eulerian framework. Such inference cannot incorporate knowledge of the life cycle and progression of extratropical cyclones across the region and is forced to make restrictive assumptions about the extremal dependence structure. We take an entirely different approach which overcomes these limitations by working in a Lagrangian framework. Specifically, we model the development of windstorms over time, preserving the physical characteristics linking the windstorm and the cyclone track, the path of local vorticity maxima, and make a key finding that the spatial extent of extratropical windstorms becomes more localized as its magnitude increases irrespective of the location of the storm track. Our model allows simulation of synthetic windstorm events to derive the joint distributional features over any set of sites giving physically consistent extrapolations to rarer events. From such simulations improved estimates of this hazard can be achieved in terms of both intensity and area affected.

Extratropical cyclones are large-scale weather systems which are often the source of extreme weather events in Northern Europe, often leading to mass infrastructural damage and casualties. Such systems create a local vorticity maxima which tracks across the Atlantic Ocean and from which can be determined a climatology for the region. While there have been considerable advances in developing algorithms for extracting the track and evolution of cyclones from reanalysis datasets, the data record is relatively short. This justifies the need for a statistical model to represent the more extreme characteristics of these weather systems, specifically their intensity and the spatial variability in their tracks. This paper presents a novel simulation-based approach to modelling the lifecycle of extratropical cyclones in terms of both their tracks and vorticity, incorporating various aspects of cyclone evolution and movement. By drawing on methods from extreme value analysis, we can simulate more extreme storms than those observed, representing a useful tool for practitioners concerned with risk assessment with regard to these weather systems.

The aim of this paper is to set out a strategy for improving the inference for statistical models for the distribution of annual maxima observed temperature data, with a particular focus on past and future trend estimation. The observed data are on a 25-km grid over the UK. The method involves developing a distributional linkage with models for annual maxima temperatures from an ensemble of regional and global climate numerical models. This formulation enables additional information to be incorporated through the longer records, stronger climate change signals, replications over the ensemble and spatial pooling of information over sites. We find evidence for a common trend between the observed data and the average trend over the ensemble with very limited spatial variation in the trends over the UK. The proposed model, which accounts for all the sources of uncertainty, requires a very high-dimensional parametric fit, so we develop an operational strategy based on simplifying assumptions and discuss what is required to remove these restrictions. With such simplifications, we demonstrate more than an order of magnitude reduction in the local response of extreme temperatures to global mean temperature changes.

ABSTRACTThe effect of large‐scale modes of climate variability on extreme UK daily rainfall, together with potential trends is investigated with non‐stationary extreme value analysis. Extreme rainfall is identified from 25 km gridded observations spanning 1958–2012 and to which generalized extreme value (GEV) distributions are fitted. The GEV location and scale parameters are assessed for their dependence on indices of the El Niño‐Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO) and Atlantic Multi‐decadal Oscillation (AMO), together with any evidence of trend. The influence of indices and trend are assessed individually and in combination, for individual months and 3‐month rolling seasons. To improve signal‐to‐noise ratio all data below 200 m elevation is pooled. The NAO is found to have the greatest impact with positive NAO reducing the likelihood of extreme rainfall from spring to autumn, but increasing it in winter. London's 50 year return level for JJA (DJF) ranges from 34(26) mm day−1 at maximum NAO to 51(24) mm day−1 for minimum NAO over the observed period. A weak ENSO influence is only found for early winter (NDJ) and no influence detected for PDO or AMO. Trends towards more extreme rainfall were found for OND and DJF; however, the inclusion of NAO resulted in reduced magnitude and significance for DJF trends. Robust trends were found for the winter half year irrespective of NAO influence, with London's 50‐year return level increasing by 6%. Extreme rainfall changes associated with NAO are consistent with NAO driven changes in extra‐tropical cyclones. Positive NAO non‐winter months have fewer less intense storms crossing the UK in contrast to winter where they are more frequent and intense. The speed of storms is also higher during positive NAO winters which can mitigate increases in the rarest events.

Understanding the impact of future heatwaves and the development of effective adaptation strategies requires knowledge of both the changes in heatwave temperatures and their durations. We develop a framework, utilising extreme value theory, which allows for the effect of a covariate on both the marginal quantiles and the temporal dependence structure of daily maximum temperatures enabling the changes in heatwave temperatures (marginal effects) to be identified separately from duration changes (dependence effects). To characterize future heatwave changes we use global mean temperature anomalies as a covariate to provide the metric for climate change. Future daily maximum temperatures and global mean temperature changes are provided by 13 general circulation models (GCMs) from the CMIP5 archive forced with predicted future emissions of radiative forcing agents from the RCP8.5 scenario. For Orleans, central France, we find that for all GCMs temporal dependence is unaffected by greenhouse gas induced climate change indicating that durations of heatwaves that exceed time varying high thresholds (i.e. the 1-year level) will not change in the future. However, all GCMs project significant changes in the temperature margins with events similar to the 2003 European heatwave increasing by 1.3°C to 2.7°C and (8.0°C to 18.7°C) for a 1°C (5°C) increase in global temperature. Collectively our results indicate there could be a significant increase in heatwave risk as the world warms with heatwaves increasing in temperature significantly faster than the global mean and local average temperatures.

In winter 2013/14 a succession of storms hit the UK leading to record rainfall and flooding in many regions including south east England. In the Thames river valley there was widespread flooding, with clean-up costs of over £1 billion. There was no observational precedent for this level of rainfall. Here we present analysis of a large ensemble of high-resolution initialised climate simulations to show that this event could have been anticipated, and that in the current climate there remains a high chance of exceeding the observed record monthly rainfall totals in many regions of the UK. In south east England there is a 7% chance of exceeding the current rainfall record in at least one month in any given winter. Expanding our analysis to some other regions of England and Wales the risk increases to a 34% chance of breaking a regional record somewhere each winter.

When assessing the risk posed by high temperatures, it is necessary to consider not only the temperature at separate sites but also how many sites are expected to be hot at the same time. Hot events that cover a large area have the potential to put a great strain on health services and cause devastation to agriculture, leading to high death tolls and much economic damage. Southeastern Australia experienced a severe heatwave in early 2009; 374 people died in the state of Victoria and Melbourne recorded its highest temperature since records began in 1859 [Nairn and Fawcett (2013)]. One area of particular interest in climate science is the effect of large-scale climatic phenomena, such as the El Nino-Southern Oscillation (ENSO), on extreme temperatures. Here, we develop a framework based upon extreme value theory to estimate the effect of ENSO on extreme temperatures across Australia. This approach permits us to estimate the change in temperatures with ENSO at important sites, such as Melbourne, and also whether we are more likely to observe hot temperatures over a larger spatial extent during a particular phase of ENSO. To this end, we design a set of measures that can be used to effectively summarise many important spatial aspects of an extreme temperature event. These measures are estimated using our extreme value framework and we validate whether we can accurately replicate the 2009 Australian heatwave, before using the model to estimate the probability of having a more severe event than has been observed.