Determining the time of emergence of climates altered from their natural state by anthropogenic influences can help inform the development of adaptation and mitigation strategies to climate change. Previous studies have examined the time of emergence of climate averages. However, at the global scale, the emergence of changes in extreme events, which have the greatest societal impacts, has not been investigated before. Based on state-of-the-art climate models, we show that temperature extremes generally emerge slightly later from their quasi-natural climate state than seasonal means, due to greater variability in extremes. Nevertheless, according to model evidence, both hot and cold extremes have already emerged across many areas. Remarkably, even precipitation extremes that have very large variability are projected to emerge in the coming decades in Northern Hemisphere winters associated with a wettening trend. Based on our findings we expect local temperature and precipitation extremes to already differ significantly from their previous quasi-natural state at many locations or to do so in the near future. Our findings have implications for climate impacts and detection and attribution studies assessing observed changes in regional climate extremes by showing whether they will likely find a fingerprint of anthropogenic climate change.

Knowledge about long‐term changes in climate extremes is vital to better understand multidecadal climate variability and long‐term changes and to place today's extreme events in a historical context. While global changes in temperature and precipitation extremes since the midtwentieth century are well studied, knowledge about century‐scale changes is limited. This paper analyses a range of largely independent observations‐based data sets covering 1901–2010 for long‐term changes and interannual variability in daily scale temperature and precipitation extremes. We compare across data sets for consistency to ascertain our confidence in century‐scale changes in extremes. We find consistent warming trends in temperature extremes globally and in most land areas over the past century. For precipitation extremes we find global tendencies toward more intense rainfall throughout much of the twentieth century; however, local changes are spatially more variable. While global time series of the different data sets agree well after about 1950, they often show different changes during the first half of the twentieth century. In regions with good observational coverage, gridded observations and reanalyses agree well throughout the entire past century. Simulations with an atmospheric model suggest that ocean temperatures and sea ice may explain up to about 50% of interannual variability in the global average of temperature extremes, and about 15% in the global average of moderate precipitation extremes, but local correlations are mostly significant only in low latitudes.

We describe and evaluate historical simulations which use the third Hadley Centre Global Environment Model in the Global Coupled configuration 3.1 (HadGEM3-GC3.1) and which form part of the UK's contribution to the sixth Coupled Model Intercomparison Project, CMIP6. These simulations, run at two resolutions, respond to historically evolving forcings such as greenhouse gases, aerosols, solar irradiance, volcanic aerosols, land use, and ozone concentrations. We assess the response of the simulations to these historical forcings and compare against the observational record. This includes the evolution of global mean surface temperature, ocean heat content, sea ice extent, ice sheet mass balance, permafrost extent, snow cover, North Atlantic sea surface temperature and circulation, and decadal precipitation. We find that the simulated time evolution of global mean surface temperature broadly follows the observed record but with important quantitative differences which we find are most likely attributable to strong effective radiative forcing from anthropogenic aerosols and a weak pattern of sea surface temperature response in the low to middle latitudes to volcanic eruptions. We also find evidence that anthropogenic aerosol forcings play a role in driving the Atlantic Multidecadal Variability and the Atlantic Meridional Overturning Circulation, which are key features of the North Atlantic ocean. Overall, the model historical simulations show many features in common with the observed record over the period 1850-2014 and so provide a basis for future in-depth study of recent climate change.Plain Language Summary Historical simulations, which successfully reproduce features of the observed climate from the end of the preindustrial period to the near-present day, contribute to our understanding of the underlying mechanisms that drive or influence climate variability and climate change. The historical simulations described in this paper use the third Hadley Centre Global Environment Model in the Global Coupled configuration 3.1 model. These simulations form part of the UK's contribution to the sixth Coupled Model Intercomparison Project. We assess various aspects of the historical climate system in our simulations against observations. This includes the evolution of global mean surface temperature, ocean heat content, sea ice extent, ice sheet mass balance, permafrost extent, snow cover, North Atlantic sea surface temperature and circulation, and decadal precipitation. The key findings include (a) that the model global mean surface temperature broadly follows the observed record, with a few quantitative differences, and (b) that the ocean circulation and sea surface temperatures of the North Atlantic are likely influenced by historical forcings. In general, the simulations respond to historically evolving influences in a similar manner to the observed world. Therefore, these simulations contribute, as part of the wider CMIP6 multimodel effort, to the understanding of the causes of observed climate change since 1850.

The relative importance of anthropogenic aerosol in decadal variations of historical climate is uncertain, largely due to uncertainty in aerosol radiative forcing. We analyze a novel large ensemble of simulations with HadGEM3‐GC3.1 for 1850–2014, where anthropogenic aerosol and precursor emissions are scaled to sample a wide range of historical aerosol radiative forcing with present‐day values ranging from –0.38 to –1.50 Wm–2. Five ensemble members are run for each of five aerosol scaling factors. Decadal variations in surface temperatures are strongly sensitive to aerosol forcing, particularly between 1950 and 1980. Post‐1980, trends are dominated by greenhouse gas forcing, with much lower sensitivity to aerosol emission differences. Most realizations with aerosol forcing more negative than about –1 Wm–2 simulate stronger cooling trends in the mid‐20th century compared with observations, while the simulated warming post‐1980 always exceeds observed warming, likelydue to a warm bias in the transient climate response in HadGEM3‐GC3.1.

Climate variability in the Pacific has an important influence on climate around the globe. In the period from 1981 to 2012, there was an observed large‐scale cooling in the Pacific. This cooling projected onto the negative phase of the Pacific Decadal Oscillation (PDO) and contributed to a slowdown in the rate of near‐surface temperature warming. However, this cooling pattern is not simulated well by the majority of coupled climate models and its cause is uncertain. We use large multi‐model ensembles from the sixth Climate Model Intercomparison Project, and an ensemble of simulations with HadGEM3‐GC3.1‐LL that is specifically designed to sample the range of uncertainty in historical anthropogenic aerosol forcing, to revisit the role of external forcings. We show that anthropogenic aerosols can drive an atmospheric circulation response via an increase in North Pacific sea level pressure and contribute to a negative PDO during this period in several global climate models. In HadGEM3, this increase in North Pacific sea‐level pressure is associated with an anomalous Rossby Wave train across the North Pacific, which is also seen in observations. Our results provide further evidence that anthropogenic aerosols may have contributed to observed cooling in the Pacific in this period. However, the simulated cooling in response to aerosol forcing is substantially weaker than the warming induced by greenhouse gases, resulting in simulations that are warming faster than observations, and further highlighting the need to understand whether models correctly simulate atmospheric circulation responses.

This study examines trends in the area affected by temperature and precipitation extremes across five large-scale regions using the climate extremes index (CEI) framework. Analyzing changes in temperature and precipitation extremes in terms of areal fraction provides information from a different perspective and can be useful for climate monitoring. Trends in five temperature and precipitation components are analyzed, calculated using a new method based on standard extreme indices. These indices, derived from daily meteorological station data, are obtained from two global land-based gridded extreme indices datasets. The four continental-scale regions of Europe, North America, Asia, and Australia are analyzed over the period from 1951 to 2010, where sufficient data coverage is available. These components are also computed for the entire Northern Hemisphere, providing the first CEI results at the hemispheric scale. Results show statistically significant increases in the percentage area experiencing much-above-average warm days and nights and much-below-average cool days and nights for all regions, with the exception of North America for maximum temperature extremes. Increases in the area affected by precipitation extremes are also found for the Northern Hemisphere regions, particularly Europe and North America.

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Gaining a better understanding of rare weather events is a major research challenge and of crucial relevance for societal preparedness in the face of a changing climate. The main focus of previous studies has been to apply a range of relatively distinct methodologies to constrain changes in the odds of those events, including both parametric statistics (extreme value theory, EVT) and empirical approaches based on large numbers of dynamical model simulations. In this study, the applicability of EVT in the context of probabilistic event attribution is explored and potential combinations of both methodological frameworks are investigated. In particular, this study compares empirical return time estimates derived from a large model ensemble with parametric inferences from the same data set in order to assess whether statements made about events in the tails are similar. Our analysis is illustrated using a case study of cold extremes and heavy rainfall in winter 2013/14 in Europe (focussing on two regions: North-West Russia and the Iberian Peninsula) for a present-day (including ‘anthropogenic’ influences) and an alternative ‘non-industrial’ climate scenario. We show that parametric inferences made about rare ‘extremes’ can differ considerably from estimates based on large ensembles. This highlights the importance of an appropriate choice of block and sample sizes for parametric inferences of the tails of climatological variables. For example, inferences based on annual extremes of daily variables are often insufficient to characterize rare events due to small sample sizes (i.e. with return periods >100>100 years). Hence, we illustrate how a combination of large numerical simulations with EVT might enable a more objective assessment of EVT parameters, such as block and sample size, for any given variable, region and return period of interest. By combining both methodologies, our case study reveals that a distinct warming of cold extremes in winter has occurred throughout Europe in the ‘anthropogenic’ relative to the non-industrial climates for given sea surface temperatures in winter 2013/14. Moreover, heavy rainfall events have become significantly more frequent and more pronounced in North and North-East Europe, while other regions demonstrate no discernible changes. In conclusion, our study shows that EVT and empirical estimates based on numerical simulations can indeed be used to productively inform each other, for instance to derive appropriate EVT parameters for short observational time series. Further, the combination of ensemble simulations with EVT allows us to significantly reduce the number of simulations needed for statements about the tails