Recently we have seen more record‐breaking temperatures being set both globally and locally. This study provides a methodology for quantifying the human influence on the changing rate of record‐breaking. Using climate model simulations with and without human influences the likelihood of setting new records can be compared. I find that climate change has been increasing the likelihood of global temperature record occurrences since the 1960s. The chance of new warm records being set, like the 2016 global temperature record and the 2014 Central England temperature record, have increased and this can be attributed to the human influence on the climate.

Record‐breaking temperatures attract attention from the media, so understanding how and why the rate of record breaking is changing may be useful in communicating the effects of climate change. A simple methodology designed for estimating the anthropogenic influence on rates of record breaking in a given time series is proposed here. The frequency of hot and cold record‐breaking temperature occurrences is shown to be changing due to the anthropogenic influence on the climate. Using ensembles of model simulations with and without human‐induced forcings, it is demonstrated that the effect of climate change on global record‐breaking temperatures can be detected as far back as the 1930s. On local scales, a climate change signal is detected more recently at most locations. The anthropogenic influence on the increased occurrence of hot record‐breaking temperatures is clearer than it is for the decreased occurrence of cold records. The approach proposed here could be applied in rapid attribution studies of record extremes to quantify the influence of climate change on the rate of record breaking in addition to the climate anomaly being studied. This application is demonstrated for the global temperature record of 2016 and the Central England temperature record in 2014.

1 Introduction The Earth has warmed over the last century and the majority of that warming is very likely due to the anthropogenic influence on the climate (Bindoff et al., 2013). The occurrence of heat extremes has also increased coincident with a reduction in cold extremes over much of the globe (Coumou & Rahmstorf, 2012; Donat et al., 2013; Hartmann et al., 2013). Extreme events, including those which break records, have the greatest impacts and provide an avenue for communicating the effects of climate variability and change. Record‐breaking events often feature in the news, e.g., the global temperature records of 2014, 2015, and 2016 (e.g., Carrington 2017) as well as local‐scale records like the hottest year on record in Central England in 2014 (e.g., BBC News, 2014). Understanding how record‐breaking events are changing due to the anthropogenic influence on the climate is important, in part because these events get so much attention from media outlets. Previous studies have examined how the rates of record breaking have changed in well‐observed areas of the world such as the US (Anderson et al., 2011; Meehl et al., 2009; Rowe & Derry, 2012), the UK (Kendon, 2014), and Australia (Lewis & King, 2015). These studies have found increased rates of hot temperature records and decreased record setting for cold temperatures in those areas of the world in recent decades. For example, Lewis and King (2015) found that from 2000 to 2014 there were 12 times as many hot record‐breaking temperatures as cold records in Australia and attributed this to anthropogenic climate change. Across the world, there were about five times more record‐breaking monthly temperatures than would be expected without a long‐term warming trend (Coumou et al., 2013) over the early 21st century. Recent global record temperatures show an even clearer climate change signal than is seen regionally, as spatial aggregation reduces variability (Fischer & Knutti, 2015; Hawkins & Sutton, 2009), such that recent global heat records would be very unlikely without the effect of human‐induced climate change (Mann et al., 2016; Zorita et al., 2008). The increased frequency in global annual temperature records had been noted previously, e.g., Bassett (1992) suggests that further global record temperatures after 1988 could be expected (there are eight global annual temperature records after 1988 in the globally infilled HadCRUT4 series; Cowtan & Way, 2014 up to and including 2016). Analysis on the changing rates of record‐breaking temperatures has been based on different statistical methods, including the use of hypothesis testing and Monte Carlo simulations, with observed temperature series compared with alternative stationary series (e.g., Benestad, 2003, 2004 ; Meehl et al., 2009). The majority of these analyses detect a change in the rates of record breaking, but do not formally attribute the change to anthropogenic influences. In general, more hot records and fewer cold records are expected in a warming climate assuming there is not a substantial change in the statistical distribution of temperature (Donat & Alexander, 2012; Lewis & King, 2017). However, local climatic changes driven by dynamical responses to a warming world could alter changes in record‐breaking rates for some regions, like the US (e.g., Palmer, 2014), beyond what might be expected from a simple shift in statistical distributions. Many heat records in the central US still date from the 1930s Dust Bowl period when unusual atmospheric flow and very dry spring periods allowed severe heat extremes to develop (Donat et al., 2016), but in most other parts of the world, current temperature records date from the last few decades. Climate change is not only increasing the likelihood of recent record heat extremes (e.g., Lewis & Karoly, 2013; Stott et al., 2004), but record‐breaking hot summers and years over previous decades are also attributable to anthropogenic climate change (King et al., 2016). Indeed, for global temperatures the last 18 record‐breaking hot years, occurring between 1937 and 2016, were all attributable to the anthropogenic influence on the climate (King et al., 2016). Previous research finds changes in the rate of record‐breaking events, and that climate change is altering the likelihood of observed record‐breaking heat extremes. However, an attribution analysis of the rate of record breaking itself based on a single time series, like global annual temperatures, has not been performed previously. This study investigates when the rates of both hot and cold temperature record breaking can be attributed to anthropogenic climate change at the global and local scales, using modeled temperature series. This is different from using specific thresholds often seen in event attribution studies. The results of this analysis will allow for statements on whether the occurrence of a record in itself is more or less likely because of anthropogenic climate change as opposed to just the temperature anomaly (e.g., King et al., 2015) that the record is associated with. Near‐real‐time event attribution after a climate extreme has occurred is becoming more common (e.g., van der Wiel et al., 2017). The approach used here could also be used in near real time to examine the question: has climate change increased the chance of this type of record occurring?

2 Data and Methods Series for the global average temperature (both HadCRUT4; Morice et al., 2012 and infilled version of HadCRUT4; Cowtan & Way, 2014) and the Central England Temperature (CET series; Parker et al., 1992) were analyzed. The HadCRUT4 and CET series were obtained from the Met Office's HadOBS website (http://www.metoffice.gov.uk/hadobs/), while the infilled global temperature series of Cowtan and Way (2014) was obtained from the University of York website (http://www‐users.york.ac.uk/~kdc3/papers/coverage2013/series.html). These data sets were analyzed for the same 1861–2016 period. Given that the infilled version of HadCRUT4 (Cowtan & Way, 2014) is more comparable to the globally complete model simulations described below, this data set is used throughout the analysis. A comparison in the number of hot and cold records with the spatially incomplete original HadCRUT4 data set (Morice et al., 2012) is made. Simulations from 16 models from the fifth phase of the Coupled Model Intercomparison project (CMIP5; Taylor et al., 2012) were used in this study (Table 1). These models all have at least three historical, one historical natural (historicalNat), one preindustrial control (piControl), and one high greenhouse gas emissions projection (RCP8.5) simulation available for analysis on the Australian National Computing Infrastructure (NCI) server. The historical simulations, analyzed for a common 1861–2005 period, were run under both natural (including solar and volcanic influences) and anthropogenic forcings (including human‐induced emissions of greenhouse gases and aerosols). The historicalNat simulations, also analyzed for the 1861–2005 period, were only forced by natural influences on the climate. The RCP8.5 runs, analyzed for 2006–2100, are simulations of the climate over the 21st century as projected under a high‐emission scenario that agrees most strongly with observed greenhouse gas emissions since 2006. The piControl simulations were only used in the event attribution component of the analysis to provide an estimate of the likelihood of record setting under internal climate variability alone. Table 1. Model simulations used in this study Model name Simulations used piControl Historical HistoricalNat RCP8.5 ACCESS1.3 1 1,2,3 1 1 Bcc‐csm1.1a 1 1,2,3 1 1 CanESM2 1 1,2,3,4,5 1,2,3,4,5 1,2,3,4,5 CCSM4 1 1,2,3,4,5,6 1,2,4,6 1,2,4,6 CESM1‐CAM5 1 1,2,3 1,2,3 1,2,3 CNRM‐CM5 1 1,2,3,4,5,6,7,8,9,10 1,2,4 1,2,4 CSIRO‐Mk3.6.0 1 1,2,3,4,5,6,7,8,9,10 1,2,3,4,5 1,2,3,4,5 GFDL‐CM3 1 1,2,3,4,5 1 1 GISS‐E2‐H 1 1,2,3,4,5 1,2 1,2 GISS‐E2‐R 1 1,2,3 1,2 1,2 HadGEM2‐ES 1 1,2,3,4,5 1,2,3,4 1,2,3,4 IPSL‐CM5A‐LRa 1 1,2,3,4,5,6 1,2,3 1,2,3 IPSL‐CM5A‐MR 1 1,2,3 1 1 MIROC‐ESM 1 1,2,3 1 1 MRI‐CGCM3 1 1,2,3 1 1 NorESM1‐M 1 1,2,3 1 1 The near‐surface air temperature “tas” was extracted and analyzed for monthly, seasonal, and annual average temperatures; however, only the results based on the annual analysis are shown here. Extreme temperature indices defined by the Expert Team on Climate Change Detection and Indices representing the highest maximum temperature (TXx) and lowest minimum temperature (TNn) in a year were extracted from CMIP5 models on the CLIMDEX website (http://www.cccma.ec.gc.ca/data/climdex/; Sillmann et al., 2013, 2013). These indices were studied for local‐scale record breaking only, as at a global scale they are of less relevance. All CMIP5 data were interpolated onto a common regular 2° grid. The methodologies used to investigate record setting through the time series are slightly different from those used in the event attribution component of the analysis. For this reason, I have split the remainder of the methods into two sections. 2.1 Methodology for Timeseries Analysis Anomalies of each index were calculated per model based on a 1961–1990 climatology of the model's historical simulations. The historicalNat and RCP8.5 anomalies were calculated relative to the 1961–1990 climatology of the equivalent historical model simulation. Records were identified in each model simulation simply as the highest or lowest value of the temperature index up until the year in question. These records were identified for each historical simulation individually for 1861–2005, conjoined historical and RCP8.5 simulations for 1861–2100 and historicalNat simulations for 1861–2005. Records were also identified in the observed global average temperature and CET series. The models were evaluated for use in this analysis in two steps. Model evaluation was based on ability to capture observed globally averaged and CET distributions, and observed record‐breaking hot temperature occurrences in these time series. First, temperature anomalies from historical model simulations were compared with observed temperature anomalies over the common 1951–2005 period when we have greatest confidence in the observational series. The anomalies from individual historical simulations and observations were tested for how likely it is that they were drawn from the same population using a Kolmogorov‐Smirnov (KS) test. If more than one‐third of the distributions from historical simulations were significantly different (p < 0.05) from the observed distributions then the model was removed from further analysis. All results shown here are based on models that passed this test (all models for global temperature and all but Bcc‐csm1.1 and IPSL‐CM5A‐LR in the case of CET). In addition, the models with very different rates of hot temperature record breaking from observed were removed as a sensitivity test. If the historical simulations of a particular model all had more hot records than were observed over the same 1861–2005 period (e.g., GISS‐E2‐H for global temperature series in Figure 1) or if all simulations had fewer hot records than observed (e.g., ACCESS1.3, also Figure 1) then these models were excluded to test the sensitivity of our results to model selection. The differences in the results from this test were negligible (not shown). Figure 1 Open in figure viewer PowerPoint Graph showing the number of hot and cold record‐breaking years globally during the 1861–2005 period in the ALL and NAT ensembles. The black circles mark the number of records in the observations (filled for the spatially complete HadCRUT4 data by Cowtan and Way, unfilled for the original HadCRUt4 data), while the colored symbols indicate the simulated number of records for each model as indicated by the legend. rr obs ) and model simulations (rr sim ) were compared with a statistically expected rate of record breaking (rr exp ) and these were defined as: (1) i is the number of years into the timeseries considered. This expected rate of record breaking is purely statistical and does not account for persistence observed in the climate system (e.g., Lewis & King, 2015 2009 (2) m is the number of records in the ensemble and n is the number of model simulations in the ensemble. Values of rr sim,ens (i) were calculated for the ensemble of all‐forcing simulations (ALL; historical and RCP8.5) and the natural‐forcings simulations (NAT; historicalNat) separately. In order to account for sampling uncertainties, these record‐setting rates were bootstrapped 10,000 times based on half of the number of simulations within each ensemble. Thus 10,000 estimates of rr sim,ens (i) were calculated and compared with a single expected ratio rr sim,ens (i) as: (3) Rates of record‐breaking temperatures (i.e., the number of annual record‐breaking temperatures per year in a given time series) from the observations () and model simulations () were compared with a statistically expected rate of record breaking () and these were defined as:whereis the number of years into the timeseries considered. This expected rate of record breaking is purely statistical and does not account for persistence observed in the climate system (e.g., Lewis & King,; Meehl et al.,). When model simulations are combined, the simulated and expected rates of record breaking for a given year in the series across the ensemble become:whereis the number of records in the ensemble andis the number of model simulations in the ensemble. Values of) were calculated for the ensemble of all‐forcing simulations (ALL; historical and RCP8.5) and the natural‐forcings simulations (NAT; historicalNat) separately. In order to account for sampling uncertainties, these record‐setting rates were bootstrapped 10,000 times based on half of the number of simulations within each ensemble. Thus 10,000 estimates of) were calculated and compared with a single expected ratio) as: (4) The simulated rates of record breaking are volatile from year to year due to the size of the model ensemble. Thus, before the simulated‐to‐expected ratios of record breaking are plotted a 5‐year smoothing is applied which is additive: (5) The ratio of simulated‐to‐expected record breaking is then simply calculated as:and 10,000 estimates are produced. These values are sorted and the 10th and 90th percentile estimates of the ratio are extracted. 2015 2015 2003 i, were defined as: (6) P NAT (i) is the probability of a record occurring in the NAT ensemble (historicalNat) and P ALL (i) is the probability of a record occurring in the ALL ensemble (historical) for the centered year of moving 21‐year windows. These probabilities were simply defined as: (7) (8) To use a method akin to a standard event attribution framework (King et al.,; Lewis & King,) to assess the climate change role in the rate of record‐breaking temperature occurrences, the fractional attributable risk (FAR; Allen,) and equivalent risk ratio (RR) were calculated on counts of records. Bootstrapping of half of the number of model simulations within the ALL and NAT ensembles was again used to generate 10,000 estimates of both quantities. FAR and RR, for a given year, were defined as:where) is the probability of a record occurring in the NAT ensemble (historicalNat) and) is the probability of a record occurring in the ALL ensemble (historical) for the centered year of moving 21‐year windows. These probabilities were simply defined as: m NAT (j) and m ALL (j) are the absolute number of records across the given bootstrapped ensemble subset in a given year, respectively, and n NAT and n ALL are the number of model years in the ALL and NAT ensemble subsets, respectively. Using shorter windows than 21 years causes the FAR and RR to be more volatile especially toward the end of the temperature series when records tend to be rarer in a stationary climate. Since the purpose here is to characterize the climate change signal, increasing sample sizes by using a 21‐year period (similar to the use of a multiyear period to represent the present climate in event attribution studies such as King et al., 2015 2013 (9) where) and) are the absolute number of records across the given bootstrapped ensemble subset in a given year, respectively, andandare the number of model years in the ALL and NAT ensemble subsets, respectively. Using shorter windows than 21 years causes the FAR and RR to be more volatile especially toward the end of the temperature series when records tend to be rarer in a stationary climate. Since the purpose here is to characterize the climate change signal, increasing sample sizes by using a 21‐year period (similar to the use of a multiyear period to represent the present climate in event attribution studies such as King et al.,; Lewis & Karoly,) is warranted. The 10th percentile FAR and RR estimates, derived from ranking the 10,000 bootstrapped FAR and RR values and extracting the 1000th value, were used to determine when an anthropogenic signal can be seen in the rate of record breaking. Ifthen, based on the model ensemble used here, there is at least 90% confidence that anthropogenic climate change has at least doubled the likelihood of a record being broken for the corresponding 21‐year window. Note, the use of centered 21‐year windows results in FAR and RR estimates only being made for the 1871–1995 period. The effect of climate change on the rate of record‐breaking temperature occurrences is assessed based on these statistics. The likelihood of record occurrences in the ALL and NAT forcings ensembles can be estimated through time. Also, the ratio of record occurrences in these ensembles to the statistically expected rate of record breaking (defined previously) may be used to investigate the evolving influence of climate change on record‐breaking occurrences through time. To assess the anthropogenic influence on record breaking at a gridbox scale, ratios of records in the ALL‐ versus NAT‐forcing ensembles for 1976–2005 (2005 being the end of the common period) were calculated. Bootstrapping of half the simulations in each ensemble was also performed for each gridbox, but, due to computational limitations, only 1000 bootstraps were used. Confidence in the simulated anthropogenic influence on rates of record breaking was then ascertained. Results are shown for locations where 90% of bootstrapped ratios were either greater than or less than one (i.e., there is at least 90% confidence that anthropogenic climate change is increasing or decreasing the likelihood of a record occurring). The effect of climate change on record‐breaking occurrences can be assessed through the use of bootstrapped ensembles of model simulations with and without anthropogenic forcings. Climate models often have different autocorrelation structures than are seen in observations, but unless the differences in serial correlation are very strong, the method should perform well (Coumou et al., 2013). The results of this study are contingent on climate model simulations, for which we have higher confidence on the global scale than locally. At the gridbox level, serial correlation structures may differ more strongly between observations and simulations, and biases are likely to be larger, than at a global scale. 2.2 Methodology for Event Attribution Analysis Here, I outline an adaptation to the method above for investigating the anthropogenic influence on a recent record‐setting event. The examples of the record warm global temperature of 2016 and the record warm CET of 2014 are used. Note that for both of the associated temperature series there are studies that have previously detected statistically significant changes. In the case of the global temperature series, there are many such analyses (including in the IPCC fifth assessment report itself; Bindoff et al., 2013) and for the CET series, there is Karoly and Stott (2006). These detectable changes in the associated timeseries influence the interpretation of the results. The presence of detected changes that have also been attributed to anthropogenic influences adds confidence to any statements made here on the role of climate change in the recent record‐setting events. For events after 2006, when many historical and historicalNat simulations end, the ALL world is estimated using the RCP8.5 simulations predominantly (given that 21‐year moving windows are applied to conjoined historical and RCP8.5 simulations of the same model and run number). For the NAT world, the piControl simulations are used after 2006 as these provide an estimate of the rate of record setting under internal climate variability alone. This is an important point of distinction for investigating record‐breaking rates, particularly cold records which often follow volcanic eruptions. Neither the RCP scenarios nor the piControl runs include volcanic eruptions, thus making them more comparable and suitable for examining recent records than using the RCP and historicalNat simulations in combination. A summary of the model experiments used for each ensemble and time period is shown in Table 2. Table 2. Model experiments used in constructing the ALL and NAT ensembles for event attribution analysis during the historical period (1861–2005) and the projected period (2006–2100) Model ensemble 1861–2005 2006–2100 ALL Historical RCP8.5 NAT HistoricalNat piControl The chance of setting a record under ALL forcings is estimated using the same method as in the time series analysis, although for one simulation per model only (for direct comparison with the single piControl simulations for each model), for 21‐year periods centered on 2016 for investigating the global temperature record and 2014 for the CET record. The same method is also applied to the NAT ensemble by estimating the probability of a record the same length of time into the corresponding piControl series as for the observed and ALL forcing series. The piControl simulations were detrended before records were identified to avoid potential issues with model drift (Sen Gupta et al., 2013). Both the ALL and NAT ensembles were again bootstrapped to estimate uncertainty on these record‐setting probabilities. The FAR and RR were then estimated and used to make the attribution statement.

3 Results 3.1 Global Temperature Records There were 17 recorded hot and only two recorded cold values in the global annual temperature series over the 1861–2005 period (Figure 1) using the infilled HadCRUT4 data (Cowtan & Way, 2014). The number of records in the original HadCRUT4 data set (Morice et al., 2012) is shown for comparison. These data sets handle missing data differently and this has been shown to have a slight influence on global temperature series (Cowtan & Way, 2014) and some evidence of that effect can be seen in the different numbers of records as shown in Figure 1. Records are counted from 1861 onward here, so sensitivity analysis to the start date was conducted by also only counting records from later dates (Figure S1 of Supporting Information S1). However, there are more hot records than cold records in the observational series regardless of the start date used. The ALL‐forcing CMIP5 simulations show far greater numbers of hot records than cold records, reproducing the observed values. The NAT ensemble contains far fewer hot annual records than the ALL ensemble, but a similar number of cold records. None of the NAT ensemble simulations contains as many global average hot temperature records as were observed over the historical period. Only when anthropogenic forcings are included do the models reproduce the observed frequency of record breaking for global annual temperatures. An anthropogenic influence only appears clearly when examining numbers of record‐breaking hot temperatures, as opposed to cold records (Figure 1). Given that records are more likely to occur near the start of the time series in a stationary climate and this is when the anthropogenic influence is weakest, this is unsurprising. Also, major volcanic eruptions increase the likelihood of record cold temperatures in both the ALL and NAT ensembles whereas no natural influence on the climate, with coherent timing across coupled model simulations, causes as strong an increase in the likelihood of record hot temperatures. The observed global annual temperature series is well captured by the ALL ensemble as opposed to the NAT ensemble which does not capture the late 20th‐century warming trend (Figure 2a). Figures 2b and 2c show the ratio of simulated to expected (statistically) hot and cold temperature record occurrences for 1861–2100 (calculated from equation 5). In the observed series, there were both hot and cold records early in the period, but in marked contrast to the cold global temperature records which end in 1862, there is a resurgence in the frequency of hot global annual temperature records from 1981 onward. The ALL ensemble captures this resurgence. From 2000 onward, the ALL ensemble suggests at least a 50‐fold greater likelihood in hot records than would be expected statistically and this ratio rises through the 21st century. Indeed, more than half the ALL ensemble contains record‐breaking hot temperatures in most years from 2050 onward (Figure 3). In comparison, the ratios are much smaller for the NAT ensemble (which ends in 2005) in the late 1990s and 2000s. The influence of anthropogenic climate change on the likelihood of hot global annual temperature records is clear with a distinguishable signal, as measured by FAR and RR estimates (calculated from equation 9), for 1926–1945 and 1967 onward (illustrated with red horizontal bars in Figure 2b). For these two periods, the analysis indicates that the net anthropogenic influence is at least doubling the likelihood of the occurrence of a record‐breaking hot year. Figure 2 Open in figure viewer PowerPoint (a) Timeseries of observed (black) annual global temperature anomalies (from a 1961–1990 climatology), and the range of simulated global temperature anomalies including natural and anthropogenic forcings (red) and natural forcings only (blue). (b), (c) The ratio (10th–90th percentile confidence interval) of simulated to expected global temperature record rates (see equation (5)) for (b) the hottest year on record and (c) the coldest year on record for ensembles of model simulations including natural and anthropogenic forcings (red and dark blue, respectively), and natural forcings only (orange and light blue, respectively). The gray bars indicate the timing of five major volcanic eruptions. The crosses show the observed hot (red) and cold (blue) records, and the magenta cross indicates both a hot and a cold record at start of the series. The horizontal red lines in (b) show when anthropogenic climate change is at least doubling (>90% confidence) the rate of hot record‐breaking temperatures. Note this calculation ends at 1995 due to the use of a centered 21‐year moving window in the FAR estimation and the historical and historicalNat simulations ending in 2005. Figure 3 Open in figure viewer PowerPoint Time series of the percentage of model simulations with (a) hot and (b) cold global annual temperature records in the ALL and NAT ensembles. The gray curve shows the statistically expected percentage in a stationary climate. The gray bars indicate the timing of five major volcanic eruptions. Anthropogenic aerosol emissions in the mid‐20th‐century act to counter the anthropogenic greenhouse gas influence and reduce the net anthropogenic effect. This, and the influence of volcanic eruptions in both the ALL and NAT ensembles, acts to prevent a net anthropogenic signal on hot temperature records being detected between 1945 and 1967. The anthropogenic signal in a reduced likelihood of cold temperature records is less straightforward to detect primarily because records are statistically rare events. The coldest years globally tend to be after major volcanic eruptions in both the NAT and ALL ensembles (Figure 2c), but the likelihood of these cold records reduces in the ALL ensemble relative to the NAT ensemble in the second half of the 20th century. 3.2 Local Temperature Records Records were identified for each gridbox as well as the global average temperature series. Figure 4 shows the ratio, for hot and cold temperature records, between the ALL and NAT ensembles over the 1976–2005 period (the last 30 years common to the historical and historicalNat simulations). Over most of the world, there are more record‐breaking hot years and fewer record cold years in the ALL ensemble compared to the NAT ensemble during this period. Over the tropics and the equatorial oceans, the ratios are generally larger for hot records and smaller for cold records suggesting that the simulations show a clearer anthropogenic influence on record‐breaking rates in these areas. In these regions, the signal‐to‐noise ratio is higher so an anthropogenic influence appears earlier in model simulations for both mean (Mahlstein et al., 2011, 2012) and extreme temperatures (King et al., 2015a). Indeed, similar ratios for hot and cold temperature records are seen in the highest annual maximum temperature, TXx (Figure S2 of Supporting Information S1) and lowest annual minimum temperature, TNn (Figure S3 of Supporting Information S1). It is worth noting that the models have not been evaluated at each gridbox in producing these maps. However, they produce results broadly consistent with those which would be expected from our physical understanding of the climate system. Figure 4 Open in figure viewer PowerPoint Maps of the best estimate ratio of (a) hot and (b) cold annual temperature record occurrences in the ALL ensemble relative to the NAT ensemble during the 1976–2005 period (where numbers greater than one indicate more records in the ALL than the NAT ensemble and vice versa). The ratios are shown where there is at least 90% confidence the ratio is greater than or less than one. An anthropogenic signal on record hot temperatures in Central England could also be seen since the 1990s, although the climate change influence is less clear than for the global average due to higher interannual variability in the series (Figure 5). Central England was chosen to investigate local‐scale temperature records due to the high‐quality observational data available there. Like for the globe as a whole, there is a warming trend that is better captured by the ALL ensemble than the NAT ensemble albeit less clearly (Figure 5). This trend has previously been attributed to anthropogenic climate change (Karoly & Stott, 2006) as has the 2014 record annual temperature (King et al., 2015). The CET series is noisier than the global average temperature series in both the observations and model simulations. Figure 5 Open in figure viewer PowerPoint As Figure 2 , but for the annual Central England Temperature series. The ratios of simulated to (statistically) expected rates of record breaking for hot years in the model simulations are lower for the CET series than for the global series (Figure 5b). However, there is still a clear anthropogenic signal from the 1990s onward as even at the local scale the influence of anthropogenic climate change begins to appear. For cold temperature records, there is less of a clear anthropogenic signal, as was the case for global cold records; however, there are fewer cold temperature records in the ALL ensemble than the NAT ensemble since the 1980s. 3.3 Record Breaking in an Event Attribution Framework The heat anomaly associated with the global temperature record of 2016 would have been very unlikely to occur in the absence of anthropogenic forcings (King et al., 2016; Mann et al., 2016), while the CET record of 2014 was also made more likely by climate change (King et al., 2015). The role of human‐induced climate change in the chance of setting a new record in itself was investigated both for the global series and the CET series. The chance of setting a heat record under internal variability alone (Figures 6a and 6b; blue histograms) is low over a century into the global and CET time series. In both cases, it is substantially much more likely to set a heat record in the ALL ensemble (Figures 6a and 6b; orange histograms). The conservative (10th percentile) FAR estimates for the probability of setting a record are roughly 0.97 for both the global temperature record of 2016 and the CET record of 2014. This equates to at least a 29‐fold increase in the likelihood of setting a record for a hot year in global temperatures in 2016 due to anthropogenic forcings. Human‐induced climate change increased the chance of setting a new heat record in the CET in 2014 by at least a factor 35. The different RRs derived from the same 10th percentile FAR estimate are simply due to the rounding in the FAR estimate presented here. Figure 6 Open in figure viewer PowerPoint Probability density functions of the chance of a hot record being set in a given year for (a) global annual temperature in piControl (blue) and all‐forcings (2006–2026; orange) simulations, and (b) Central England temperature in piControl (blue) and all‐forcing (2004–2024; orange) simulations. The dashed lines in (a) and (b) indicate the statistical chance of setting a record in a stationary series. The autocorrelation structure of (c) global annual temperature, and (d) Central England temperature in the observed series (black circle) and CMIP5 preindustrial control simulations (colored symbols matching those in Figure 1 ). The chance of setting a record is slightly higher for the globe than for Central England in the piControl simulations. The reason for this is that a record in the simulated global temperature series, under internal variability alone, is more likely to be followed by another record compared with the CET series. The effect of this persistence can also be seen through differences in the autocorrelation structures in the piControl simulations and the observed (detrended) series (Figures 6c and 6d) between the global and central England series.

4 Conclusions This study proposes a simple methodology to attribute the rate of record breaking in a single time series, like global annual temperature, directly to anthropogenic climate change as opposed to attribution of the temperature anomalies that occurred. The method is then adapted slightly for the estimation of the role of climate change in setting a specific record. The methodology proposed here combines aspects of those used by Lewis and King (2015) and King et al. (2016). The observed number of hot record‐breaking global annual temperature occurrences for 1861–2005 is only captured by models which include anthropogenic as well as natural forcings. Rates of record‐breaking temperatures in the CMIP5 model ensembles are strongly affected by volcanic eruptions. When anthropogenic forcings are included, the rate of hot record breaking in the global annual temperature series rises. For 1926–1945 and 1967 onward, anthropogenic climate change has at least doubled the likelihood of record‐breaking hot years globally. During the middle of the 20th century, the net human influence was weakened as anthropogenic aerosols counteracted the enhanced greenhouse gas effect. In general, the FAR estimates, and associated RRs, for frequency of records are lower than for observed temperature anomalies because modern global temperature anomalies would be virtually impossible in a natural world simulation, but a record within a simulation can still be set toward the end of the 20th century. Record‐breaking cold years globally do not occur in the late 20th century in the ensemble including anthropogenic influences. In contrast, in the NAT ensemble, there is an enhanced likelihood of record cold years after the Pinatubo eruption of 1991. Overall, it is harder to attribute lack of cold records to anthropogenic climate change than it is the abundance of recent heat records due to the relatively rare nature of records more than 100 years into the timeseries. The lack of cold records (often in both the ALL and NAT ensembles) causes the FAR and RR statistics to be unstable. Model simulations produce clearer anthropogenic signals in record‐breaking rates over the tropics than high latitudes and also over the oceans compared with land areas. These are the areas with higher signal‐to‐noise ratios (Hawkins & Sutton, 2012), so a greater likelihood of records in a warming climate would be expected there compared with areas of stronger variability. These regions experienced the earliest emergence of a climate change signal (King et al., 2015), and also tend to have contributed to anthropogenic greenhouse gas emissions less than higher latitude countries (Mahlstein et al., 2011) as well as being more vulnerable to the effects of climate change through lack of wealth (Harrington et al., 2016). A similar effect can be seen when looking at temperature extremes like the hottest day and coldest night of the year, albeit with less of a homogeneous pattern (Figures S2 and S3 of Supporting Information S1). Focussing on Central England, where high‐quality observations are available, there are signs of increased likelihood of record‐breaking hot temperatures toward the end of the 20th century, but they are not as clear as for the global average (Figure S3 of Supporting Information S1). Overall, the climate change effect on rates of record‐breaking temperatures, like for other climate variables (e.g., King et al., 2015; Fischer et al., 2013; Hawkins & Sutton, 2009), is clearer on larger spatial scales because noise is reduced on these scales. Attribution analyses of the heat records set in 2016 in global temperatures and 2014 in the CET yield clear results. In both cases, anthropogenic climate change substantially increased the probability of a new record being set. This work is primarily based on climate model simulations and is therefore subject to their limitations. While the climate models used here have been evaluated, there are underlying assumptions about their ability to realistically capture interannual to multidecadal variability. A significant bias consistent across most of the climate models would influence the results. This is less likely for the global analysis, for which a two‐step validation against observed data showed models performing well. The presence of previous studies finding detectable changes in global and CETs and attributing those to anthropogenic influences strengthens confidence in those results. In conclusion, a simple methodology for attributing both the rates of record‐breaking temperatures and recently broken records to anthropogenic climate change is proposed. This study shows that anthropogenic influences have increased the likelihood of record‐breaking hot years globally with a clear climate change signal extending back decades. The findings of this study may be used to communicate the fact that climate change is not only affecting temperature extremes but is also altering the likelihood of record breaking itself.

Acknowledgments The author thanks the two anonymous reviewers for their constructive comments and useful discussions. The author also thanks David Karoly for useful discussions. This research was supported by the ARC Centre of Excellence for Climate System Science (grant CE 110001028) and the NCI National Facility in Australia. The author acknowledges the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and also thanks the climate modelling groups for producing and making available their model output. For CMIP ,the US Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. The CMIP5 data used here are available through multiple Earth System Grid Federation (ESGF) systems across the world. The author also thanks the Met Office for providing the annual CET and HadCRUT4 global temperature series through the HadOBS website (http: metoffice.gov.uk/hadobs/). The author thanks Kevin Cowtan and Robert Way for constructing and archiving their global temperature series available through the University of York website (http://www‐users.york.ac.uk/~kdc3/papers/coverage2013/series.html).

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