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Understanding how the statistical properties of daily rainfall will respond to a warming climate requires ensembles of climate model data which are much larger than those typically available from existing centennial-scale modelling experiments. While such centennial-scale experiments are very useful to explore scenario uncertainty in twenty-first century climate, ensemble size constraints often result in regional climate change assessments restricting their focus to annual- or season-mean rainfall projections without providing robust information about changes to the most extreme events. Here, we make use of multi-thousand member ensembles of regional climate model output from the Weather@Home project to explicitly resolve how the wettest and driest days of the year over New Zealand will respond to simulations of a 3 °C world, relative to simulations of the climate of the recent past (2006–15). Using a novel framework to disentangle changes during the wettest and driest days of the year, we show that many regions which show negligible change in annual mean rainfall are in fact experiencing significant changes in the amount of rain falling during both the wettest and driest spells. Exploring these changes through the lens of drought risk, we find many agricultural regions in New Zealand will face significant changes in the frequency of low-rainfall extremes in a warmer world.

The emergence of a climate change signal relative to background variability is a useful metric for understanding local changes and their consequences. Studies have identified emergent signals of climate change, particularly in temperature-based indices with weaker signals found for precipitation metrics. In this study, we adapt climate analogue methods to examine multivariate climate change emergence over the historical period. We use seasonal temperature and precipitation observations and apply a sigma dissimilarity method to demonstrate that large local climate changes may already be identified, particularly in low-latitude regions. The multivariate methodology brings forward the time of emergence by several decades in many areas relative to analysing temperature in isolation. We observed particularly large departures from an early-20th century climate in years when the global warming signal is compounded by an El Niño-influence. The latitudinal dependence in the emergent climate change signal means that lower-income nations have experienced earlier and stronger emergent climate change signals than the wealthiest regions. Analysis based on temperature and precipitation extreme indices finds weaker signals and less evidence of emergence but is hampered by lack of long-running observations in equatorial areas. The framework developed here may be extended to attribution and projections analyses.

The Coupled Model Intercomparison Project Phase 6 (CMIP6) model ensemble projects climate change emerging soonest and most strongly at low latitudes, regardless of the emissions pathway taken. In terms of signal-to-noise (S/N) ratios of average annual temperatures, these models project earlier and stronger emergence under the Shared Socio-economic Pathways than the previous generation did under corresponding Representative Concentration Pathways. Spatial patterns of emergence also change between generations of models; under a high emissions scenario, mid-century S/N is lower than previous studies indicated in Central Africa, South Asia, and parts of South America, West Africa, East Asia, and Western Europe, but higher in most other populated areas. We show that these global and regional changes are caused by a combination of higher effective climate sensitivity in the CMIP6 ensemble, as well as changes to emissions pathways, component-wise effective radiative forcing, and region-scale climate responses between model generations. We also present the first population-weighted calculation of climate change emergence for the CMIP6 ensemble, quantifying the number of people exposed to increasing degrees of abnormal temperatures now and into the future. Our results confirm the expected inequity of climate change-related impacts in the decades between now and the 2050 target for net-zero emissions held by many countries. These findings underscore the importance of concurrent investments in both mitigation and adaptation.

Atmospheric rivers (ARs) are narrow and elongated regions of enhanced horizontal water vapour transport. Considerable research on understanding Northern Hemisphere ARs and their relationship with extreme precipitation has shown that ARs have a strong association with heavy rainfall and flooding. While there has been very little work on ARs in the Southern Hemisphere, global climatologies suggest that ARs are equally as common in both hemispheres. New Zealand in particular is located in a region of high AR frequency. This study aims to test the hypothesis that ARs play a significant role in heavy precipitation and flooding events in New Zealand. We used a recently developed AR identification method and daily station data across New Zealand to test for the concurrence of ARs and extreme rainfall. We found that, at each of the eleven stations analysed, at least seven to all ten of the top ten heaviest precipitation days between 1980 and 2018 were associated with AR conditions. Nine of the ten most damaging floods in New Zealand between 2007 and 2017 occurred during AR events. These results have important implications for understanding extreme rainfall in New Zealand, and ultimately for predicting some of the most hazardous events in the region. This work also highlights that more research on ARs in New Zealand is needed.

New Zealand is one of many higher latitude countries where extreme heat is perceived to be a less consequential impact of climate change, by virtue of its relatively cool climate. Consequently, metrics to quantify the impacts of extreme heat in New Zealand have not kept pace with wider improvements in heatwave definitions. This study evaluates different methods to quantify extreme heat in New Zealand, with a view to improve the knowledge base underpinning future climate change risk assessments. Specifically, this analysis (1) reveals which of New Zealand’s purportedly hottest years in the satellite era are robust to different definitions of extreme heat; (2) introduces a new method of quantifying extreme heat which is applicable across different regions, and serves equally well whether an analysis is contextualised relative to the past (attribution) or for the future (projections); (3) detects previously unidentified heatwaves over recent decades; (4) identifies locally significant increases in extreme heat and the potential lengthening of summer months after only 0.5 °C of global warming; and (5) discusses further research priorities to better understand the impacts of extreme heat in New Zealand over the coming decades.

Signal-to-noise (S/N) ratios are a useful method to assess the significance of future climate change relative to past experiences. Most assessments of climate change emergence have focused on S/N ratios of annual mean temperatures. However, averaging the daily experiences of weather across space or time removes the climate variability actually felt by individuals, and thus presents a less informative view of the speed of current climate change. For example, S/N ratios of annual-mean temperatures experienced by the global population after only 1 °C of warming are larger than emergent changes in daily temperatures after 3 °C of warming, and generally four times more significant when comparing the same warming threshold. Here, I examine the emergence of S/N ratios in temperature at decision-relevant scales, with a focus on daily temperatures where people live. I find that 2 °C of global warming will lead to between 30% and >90% of the global population experiencing the emergence of unusual daily temperatures (>1 σ ), while it is very unlikely (90% confidence) that more than 60% of the global population will also experience the emergence of unfamiliar daily temperatures (>2 σ ).

This study investigates the future changes in dangerous extreme precipitation event in South America, using the multi-model ensemble simulations from the HAPPI experiments. The risks of dangerous extreme precipitation events occurrence, and changes in area and population exposure are quantified. Our results show that the likelihood of dangerous extreme precipitation increases in large parts of South America under future warming; changes in extreme precipitation are nonlinear with increasing global mean temperatures; and exposure plays a minor role compared to hazard. In all the models, limiting warming to 1.5 ° C as opposed to 2 ° C shows a general reduction in both area and population exposure to dangerous extreme precipitation throughout South America. The southeast region of South America exhibited the highest multi-model median percentage of avoided area exposure at 13.3%, while the southwest region shows the lowest percentage at 3.1%. Under all shared socioeconomic pathways, South America Monsoon region and southern South America region yielded the highest multi-model median percentage of avoided population exposure (>10%). The strong spatial heterogeneity in projected changes in all the models highlights the importance of considering location-specific information when designing adaptation measures and investing in disaster preparedness.

Population ageing, an increase in the older age group’s portion of the total population, worsens the heat tolerance of a society. However, impacts of ageing on the social exposure to projected unprecedented hot summers (UHSs) are uncertain. We show that a shifting of the population distribution towards older ages amplifies the vulnerability of a country to the increasing frequency of UHSs as a result of warming during 2040–2070, especially in most populated regions such as China, India, and sub-Saharan countries. The warming scenarios from Representative Concentration Pathway (RCP) 8.5 are combined with population scenarios from three Shared Socio-economic Pathways (SSPs) SSP2, SSP3, and SSP5 together to estimate the exposure to UHSs. The ageing-driven increase in the exposure of elderly to UHSs ranges 51–198, 91–261, and 47–156 million in China, India, and sub-Saharan countries, respectively, between population scenarios. In China, with decreasing total population, the exposure to UHSs will be increased by rapid population ageing. In India and sub-Saharan countries, the potential of ageing to raise the exposure to UHSs will be even larger than that of warming. In contrast, in aged societies with slow ageing trend, e.g. United States and Europe, the warming mainly increases the exposure to UHSs. Our results suggest the changing age structure could exacerbate a country’s heat vulnerability despite limiting warming to a certain level in the future.

Global-average temperatures are a powerful metric for both long-term climate change policy, and also to measure the aggregate fluctuations in weather experienced around the world. However, here we show how the consideration of anomalies in annual temperatures at the global land-average scale, particularly during extremely hot years, tends to overestimate the perceived severity of extreme heat actually felt by local communities during these events. Thus, when global-mean temperatures are used as a proxy to infer the role of climate change on the likelihood of witnessing hot years, the component of extreme event risk attributed to human influence can also be overstated. This study suggests multiple alternative approaches to characterise extreme weather events which have complex spatial signatures, each of which improve the representation of perceived experiences from the event when compared with the default approach of using area-averaged time-series. However, as the definition of an extreme event becomes more specific to the observed characteristics witnessed, changes are needed in the way researchers discuss the likelihood of witnessing ‘similar events’ with future climate change. Using the example of the 2016 hot year, we propose an alternative framework, termed the ‘Time of Maximum Similarity’, to show that events like the record-breaking annual temperatures of 2016 are most likely to be witnessed between 2010–2037, with hot years thereafter becoming significantly more severe than the heat of 2016.

We develop a new index which maps relative climate change contributions to relative emergent impacts of climate change. The index compares cumulative emissions data with patterns of signal-to-noise ratios ( S / N ) in regional temperature (Frame et al 2017 Nat. Clim. Change 7 407–11). The latter act as a proxy for a range of local climate impacts, so emergent patterns of this ratio provide an informative way of summarising the regional disparities of climate change impacts. Here we combine these with measures of regional/national contributions to climate change to develop an ‘emissions-emergence index’ (EEI) linking regions’/countries’ contributions to climate change with the emergent regional impacts of climate change. The EEI is a simple but robust indicator which captures relative contributions to and regional impacts from climate change. We demonstrate the applicability of the EEI both for discussions of historical contributions and impacts, and for considering future relative contributions and impacts, and examine its utility in the context of existing related metrics. Finally, we show how future emissions pathways can either imply a growth or reduction of regional climate change inequalities depending on the type and compositions of socioeconomic development strategies.

Understanding how continuing increases in global mean temperature will exacerbate societal exposure to extreme weather events is a question of profound importance. However, determining population exposure to the impacts of heat extremes at 1.5 °C and 2 °C of global mean warming requires not only (1) a robust understanding of the physical climate system response, but also consideration of (2) projected changes to overall population size, as well as (3) changes to where people will live in the future. This analysis introduces a new framework, adapted from studies of probabilistic event attribution, to disentangle the relative importance of regional climate emergence and changing population dynamics in the exposure to future heat extremes across multiple densely populated regions in Southern Asia and Eastern Africa (SAEA). Our results reveal that, when population is kept at 2015 levels, exposure to heat considered severe in the present decade across SAEA will increase by a factor of 4.1 (2.4–9.6) and 15.8 (5.0–135) under a 1.5°- and 2.0°-warmer world, respectively. Furthermore, projected population changes by the end of the century under an SSP1 and SSP2 scenario can further exacerbate these changes by a factor of 1.2 (1.0–1.3) and 1.5 (1.3–1.7), respectively. However, a large fraction of this additional risk increase is not related to absolute increases in population, but instead attributed to changes in which regions exhibit continued population growth into the future. Further, this added impact of population redistribution will be twice as significant after 2.0 °C of warming, relative to stabilisation at 1.5 °C, due to the non-linearity of increases in heat exposure. Irrespective of the population scenario considered, continued African population expansion will place more people in locations where emergent changes to future heat extremes are exceptionally severe.

Increasing risks of extreme weather events are the most noticeable and damaging manifestation of anthropogenic climate change. In the aftermath of an extreme event, policymakers are often called upon to make timely and sensitive decisions about rebuilding and managing present and future risks. Information regarding whether, where, and how present day and future risks are changing is needed to adequately inform these decisions. But this information is often not available on the temporal and spatial scales decisions are made. In particular, decision makers require information about both historical changes and plausible future changes in the severity and frequency of extreme weather in a seamless way. However, applying the same methods from event attribution to future projections by defining events based on present day frequency of occurrence leads to potentially misleading estimates of future changes in a warmer climate. We demonstrate that this is fundamentally a consequence of risk ratios saturating at different values. This study investigates the circumstances under which present-day attribution frameworks become ill-suited for characterising changes in future extremes, before discussing what alternative frameworks may be more useful to inform stakeholders about what additional risks from extreme weather events will emerge in a warmer world.

A common proxy for the adaptive capacity of a community to the impacts of future climate change is the range of climate variability which they have experienced in the recent past. This study presents an interpretation of such a framework for monthly temperatures. Our results demonstrate that emergence into genuinely ‘unfamiliar’ climates will occur across nearly all months of the year for low-income nations by the second half of the 21st century under an RCP8.5 warming scenario. However, high income countries commonly experience a large seasonal cycle, owing to their position in the middle latitudes: as a consequence, temperature emergence for transitional months translates only to more-frequent occurrences of heat historically associated with the summertime. Projections beyond 2050 also show low-income countries will experience 2–10 months per year warmer than the hottest month experienced in recent memory, while high-income countries will witness between 1–4 months per year hotter than any month previously experienced. While both results represent significant departures that may bring substantive societal impacts if greenhouse gas emissions continue unabated, they also demonstrate that spatial patterns of emergence will compound existing differences between high and low income populations, in terms of their capacity to adapt to unprecedented future temperatures.

Understanding how the emergence of the anthropogenic warming signal from the noise of internal variability translates to changes in extreme event occurrence is of crucial societal importance. By utilising simulations of cumulative carbon dioxide (CO _2 ) emissions and temperature changes from eleven earth system models, we demonstrate that the inherently lower internal variability found at tropical latitudes results in large increases in the frequency of extreme daily temperatures (exceedances of the 99.9th percentile derived from pre-industrial climate simulations) occurring much earlier than for mid-to-high latitude regions. Most of the world’s poorest people live at low latitudes, when considering 2010 GDP-PPP per capita; conversely the wealthiest population quintile disproportionately inhabit more variable mid-latitude climates. Consequently, the fraction of the global population in the lowest socio-economic quintile is exposed to substantially more frequent daily temperature extremes after much lower increases in both mean global warming and cumulative CO _2 emissions.