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Summary: Extreme weather events lead to many adverse societal, economic, and environmental consequences. Anthropogenic climate change has been identified as a factor that may have already increased the frequency and intensity of these events. The methods of extreme event attribution (EEA) that quantify the extent to which climate change affected specific recent extreme weather events in the past 20 years have been combined more recently with socio-economic impact data to quantify extreme weather’s impacts attributable to climate change. This extreme event impacts attribution (EEIA) is a quickly developing field that considers which kinds of questions about the impacts of climate change on extreme weather events we should ask, what methods are best suited to answer them, how to interpret the results these methods provide, and what purpose these results can serve. We discuss the methods of EEIA, review EEIA results, and discuss their implications and potential uses.

This paper investigates marine heatwave (MHW) characteristics in New Zealand's Earth System Model (NZESM) simulations for present-day conditions and how they are projected to change in the future in relation to anthropogenic greenhouse gas emissions. Three emission scenarios following the state of the art shared-socioeconomic pathways (SSPs, SSP1 2.6, SSP2 4.5, and SSP3 7.0) are each evaluated with a set of three ensemble members. These analyses are focused on the ocean around New Zealand, where NZESM captures boundary currents and mesoscale eddies, due to its high-resolution nested ocean grid. For present-day conditions, the model overestimates MHW intensity and underestimates the number of annual MHW days for subtropical waters, while some smaller positive biases are present in subantarctic waters compared to observations. Despite this, NZESM agrees with the observational pattern that more intense MHWs and more annual MHW days are found in subtropical waters compared to subantarctic waters. NZESM projects that MHW intensity will increase more strongly in subtropical waters compared to subantarctic waters, while the largest changes in annual MHW days are projected south of Australia and the Tasman Sea in the Subtropical Front (STF) frontal region, which suggests a southward shift of the STF under increased greenhouse gas emissions. Results using a high-emission scenario (SSP3 7.0) show an increase between 80 and 100% of median MHW intensities by the end of the century relative to the present-day for all analyzed coastal regions, and MHW conditions could become permanent year-round by the end of the century.

We examine the effect of the 20th and recent 21st century anthropogenic climate change on high temperature extremes as simulated by four global atmospheric general circulation models submitted to the Climate of the 20th Century Plus Detection and Attribution project. This coordinated experiment is based upon two large ensembles simulations for each participating model. The “world that was” simulations are externally forced as realistically as possible. The “world that might have been” is identical except that the influence of human forcing is removed but natural forcing agents and variations in ocean and sea ice are retained. We apply a stationary generalized extreme value analysis to the annual maxima of the three day average of the daily maximum surface air temperature, finding that long period return values have been increased by human activities between 1 and 3 °C over most land areas. Corresponding changes in the probability of achieving long period non-industrial return values in the industrialized world are also presented. We find that most regions experience increases in the frequency and intensity of extremely hot three day periods, but anthropogenic sulfate aerosol forcing changes locally can decrease these measures of heat waves in some models.

A growing field of research aims to characterise the contribution of anthropogenic emissions to the likelihood of extreme weather and climate events. These analyses can be sensitive to the shapes of the tails of simulated distributions. If tails are found to be unrealistically short or long, the anthropogenic signal emerges more or less clearly, respectively, from the noise of possible weather. Here we compare the chance of daily land-surface precipitation and near-surface temperature extremes generated by three Atmospheric Global Climate Models typically used for event attribution, with distributions from six reanalysis products. The likelihoods of extremes are compared for area-averages over grid cell and regional sized spatial domains. Results suggest a bias favouring overly strong attribution estimates for hot and cold events over many regions of Africa and Australia, and a bias favouring overly weak attribution estimates over regions of North America and Asia. For rainfall, results are more sensitive to geographic location. Although the three models show similar results over many regions, they do disagree over others. Equally, results highlight the discrepancy amongst reanalyses products. This emphasises the importance of using multiple reanalysis and/or observation products, as well as multiple models in event attribution studies.

Heatwaves and extreme temperatures during summer (April–May) in India have profound implications on public health, mortality, water availability, and productivity of labourers. However, how the frequency of the hottest summers in observed record (1951–2015) will change under the warming climate in India is not well explored. Using observations from the India Meteorological Department, we show that mean maximum summer temperature has increased significantly in three (arid, monsoon, and savannah) out of five major climatic regions of India during 1951–2015. We identify the hottest summer in the observed record in the five climatic regions in India. The arid, cold, and temperate regions experienced the hottest summer in 2010 while monsoon and Savannah regions witnessed the hottest summer in 1979 and 1973, respectively. Based on simulations from the Climate of 20th Century Plus (C20C+) Detection and Attribution project, we show that the regional hottest summer of 2010 can be attributed to the anthropogenic warming. We then use simulations of a large (2000 year) ensemble of the EC-Earth model to estimate the exceedance probability of the observed hottest summer in the present climate, 2 °C and 3 °C warming worlds in India. The exceedance probability of the observed hottest summers shows a rise of more than seven and twenty-fold in the 2 °C and 3 °C warming world, respectively, compared to the present climate. The projected increases in the frequency of the hot summers and associated heatwave days will pose great societal challenges in the future in India.

Extreme event attribution (EEA) information is increasingly in demand from climate services. EEA messages can: raise awareness about the effect climate change has already imposed, inform climate change liability conversations, and be combined with climate projections to inform adaptation. However, due to limitations in observations, models and methods, there are barriers towards operationalising EEA in practice. Operational services will need EEA to be done transparently and using preset formats. Here we review recent experience and practice in EEA in Australia and New Zealand with a view to inform the design of an EEA component of climate services. We present a flow chart of the processes involved, noting particular care is needed on the trigger, event definition, and climate model evaluation, with effective stage gates. We also promote the use of tailored causal network diagrams as a standard tool to inform an EEA study and communicate results, with particular care needed for messages on events with lower confidence or complex sets of influences, including tropical cyclones and extratropical cyclones. We suggest that extending EEA to impact attribution is essential for making EEA messages salient but requires an uplift in forming interdisciplinary teams and in granular exposure and vulnerability datasets and is likely to raise new interdisciplinary methodological questions. Finally, we suggest communication of EEA messages can learn more from its origins in medical epidemiology.

India has witnessed some of the most devastating extreme precipitation events, which have affected urban transportation, agriculture, and infrastructure. Despite the profound implications and damage due to extreme precipitation events, the influence of anthropogenic warming on the intensity and frequency of extreme precipitation events over India remains poorly constrained. Here using the gridded observations and simulations from the Coupled model intercomparison project 5 (CMIP5) and Climate of 20th century plus (C20C+) detection and attribution (D&A) project, we show that the frequency and intensity of extreme precipitation events have increased in India during the last few decades. Along with the extreme precipitation, dew point temperature has also increased during 1979–2015. The scaling relationship between extreme precipitation and dew point temperature shows a super (more than 7% increase per unit rise in dew point temperature) Clausius-Clapeyron (C-C) relationship for the majority of south India. Moreover, southern and central India show a higher (10%/°C) scaling relationship than north India (3.5%/°C). Our analysis using the Hist (historic) and HistNat (historic natural) simulations from the CMIP5 and C20C+ projects confirms an increase in the frequency of extreme precipitation events under the anthropogenic warming. Moreover, we show that 1–5 day precipitation maxima at 5–500 year return period increases (10–30%) under the anthropogenic warming. The frequency of precipitation extremes is projected to rise more prominently in southern and central India in the mid and end of the 21st century under the representative concentration pathway (RCP) 8.5. Our results show a significant contribution of anthropogenic warming in the rise of the frequency of extreme precipitation, which has implications for infrastructure, agriculture, and water resources in India.