Extreme weather and climate‐related events occur in a particular place, by definition, infrequently. It is therefore challenging to detect systematic changes in their occurrence given the relative shortness of observational records. However, there is a clear interest from outside the climate science community in the extent to which recent damaging extreme events can be linked to human‐induced climate change or natural climate variability. Event attribution studies seek to determine to what extent anthropogenic climate change has altered the probability or magnitude of particular events. They have shown clear evidence for human influence having increased the probability of many extremely warm seasonal temperatures and reduced the probability of extremely cold seasonal temperatures in many parts of the world. The evidence for human influence on the probability of extreme precipitation events, droughts, and storms is more mixed. Although the science of event attribution has developed rapidly in recent years, geographical coverage of events remains patchy and based on the interests and capabilities of individual research groups. The development of operational event attribution would allow a more timely and methodical production of attribution assessments than currently obtained on an ad hoc basis. For event attribution assessments to be most useful, remaining scientific uncertainties need to be robustly assessed and the results clearly communicated. This requires the continuing development of methodologies to assess the reliability of event attribution results and further work to understand the potential utility of event attribution for stakeholder groups and decision makers. WIREs Clim Change 2016, 7:23–41. doi: 10.1002/wcc.380For further resources related to this article, please visit the WIREs website.

Meteorological drought in the Hadley Centre global climate model is assessed using the Palmer Drought Severity Index (PDSI), a commonly used drought index. At interannual time scales, for the majority of the land surface, the model captures the observed relationship between the El Niño–Southern Oscillation and regions of relative wetness and dryness represented by high and low values of the PDSI respectively. At decadal time scales, on a global basis, the model reproduces the observed drying trend (decreasing PDSI) since 1952. An optimal detection analysis shows that there is a significant influence of anthropogenic emissions of greenhouse gasses and sulphate aerosols in the production of this drying trend. On a regional basis, the specific regions of wetting and drying are not always accurately simulated. In this paper, present-day drought events are defined as continuous time periods where the PDSI is less than the 20th percentile of the PDSI distribution between 1952 and 1998 (i.e., on average 20% of the land surface is in drought at any one time). Overall, the model predicts slightly less frequent but longer events than are observed. Future projections of drought in the twenty-first century made using the Special Report on Emissions Scenarios (SRES) A2 emission scenario show regions of strong wetting and drying with a net overall global drying trend. For example, the proportion of the land surface in extreme drought is predicted to increase from 1% for the present day to 30% by the end of the twenty-first century.

Socio-economic stress from the unequivocal warming of the global climate system 1 could be mostly felt by societies through weather and climate extremes 2 . The vulnerability of European citizens was made evident during the summer heatwave of 2003 (refs 3,4) when the heat-related death toll ran into tens of thousands 5 . Human influence at least doubled the chances of the event according to the first formal event attribution study 6 , which also made the ominous forecast that severe heatwaves could become commonplace by the 2040s. Here we investigate how the likelihood of having another extremely hot summer in one of the worst a ected parts of Europe has changed ten years after the original study was published, given an observed summer temperature increase of 0.81 K since then. Our analysis benefits from the availability of new observations and data from several new models. Using a previously employed temperature threshold to define extremely hot summers, we find that events that would occur twice a century in the early 2000s are now expected to occur twice a decade. For the more extreme threshold observed in 2003, the return time reduces from thousands of years in the late twentieth century to about a hundred years in little over a decade.Despite the slowdown in the global mean temperature increase since the late 1990s (refs 7-9), hot temperature extremes have continued to warm on both global and regional scales 10,11 . Severe heatwaves in the past decade such as the ones in Moscow in 2010 (refs 12,13), Texas in 2011 (ref. 14) and the Australian 'angry summer' of 2012-2013 15 were characterized by long duration, large spatial extent and catastrophic impacts. Research on event attribution aims to identify the drivers of such extreme events and determine how possible causes such as human influence on the climate might have altered their odds [16][17][18] . In this paper we revisit the first study of this kind 6 that investigated the 2003 European heatwave and carry out a new analysis that is now extended to the present day. As in the original study, we concentrate on summer temperatures (average over June-August) in the land area bounded by 10 • W-40 • E and 30 • -50 • N, which, among a number of predefined climatic regions 19 , was mostly affected by the 2003 heatwave. The use of a pre-defined region helps minimize selection bias. The selected area largely includes the countries where heat-related mortality peaked (France, Germany and Italy), but is more extensive.Summer temperature time series constructed with the best estimate of the CRUTEM4 observational data set 20 show that the 2003 record was subsequently broken in 2012 (Fig. 1). Although a hot summer in the region cannot be directly linked to heatwave damage (for example, heatwave impacts in 2012 were less notable than in 2003), as records are being broken in a warming climate, hotter summers are generally expected to be associated with more severe impacts. Our analysis examines how the likelihood of very warm summers in the region has changed between the ...

[1] We have carried out an investigation into the causes of changes in near-surface temperatures from 1860 to 2010. We analyze the HadCRUT4 observational data set which has the most comprehensive set of adjustments available to date for systematic biases in sea surface temperatures and the CMIP5 ensemble of coupled models which represents the most sophisticated multi-model climate modeling exercise yet carried out. Simulations that incorporate both anthropogenic and natural factors span changes in observed temperatures between 1860 and 2010, while simulations of natural factors do not warm as much as observed. As a result of sampling a much wider range of structural modeling uncertainty, we find a wider spread of historic temperature changes in CMIP5 than was simulated by the previous multi-model ensemble, CMIP3. However, calculations of attributable temperature trends based on optimal detection support previous conclusions that human-induced greenhouse gases dominate observed global warming since the mid-20th century. With a much wider exploration of model uncertainty than previously carried out, we find that individually the models give a wide range of possible counteracting cooling from the direct and indirect effects of aerosols and other non-greenhouse gas anthropogenic forcings. Analyzing the multi-model mean over 1951-2010 (focusing on the most robust result), we estimate a range of possible contributions to the observed warming of approximately 0.6 K from greenhouse gases of between 0.6 and 1.2 K, balanced by a counteracting cooling from other anthropogenic forcings of between 0 and -0.5 K.Citation: Jones, G. S., P. A. Stott, and N. Christidis (2013), Attribution of observed historical near-surface temperature variations to anthropogenic and natural causes using CMIP5 simulations,

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