1 Introduction

Exposure to extreme climate events such as floods, droughts, heatwaves, and cold waves have far‐reaching consequences for natural and human systems (Murray & Ebi, 2012). Since 1980, the United States (U.S.) has sustained a total of $1.75 trillion in losses from climate‐related disasters, along with irreparable damage to tens of thousands of human lives (NOAA, 2020). Of these damages, droughts have cost the U.S. an estimated $9.6 billion per event, with a total of $250 billion in losses from 1980 to 2019 (NOAA, 2020). Furthermore, heat‐ and cold‐related exposure have generated an average of two‐thousand deaths annually in the U.S. (Berko et al., 2014).

There is now clear evidence that these and other forms of climate‐related threats to the economy, human health, and ecosystems are on the rise (Duffy et al., 2019). For example, the U.S. has witnessed a substantial increase in billion‐dollar disasters during the past five years (2015 to 2019: 13.8 events per year) compared to the long‐term average (1980 to 2019: 6.5 events per year). During this time, indirect and direct fatalities have increased by 233% when compared to the long‐term average (NOAA, 2020). These recent trends motivate the need for a comprehensive understanding of potential changes in population exposure to climate extremes within the context of regional and local climate variability.

Numerous scientific studies have investigated future changes in climate extremes over the U.S. (Murray & Ebi, 2012; USGCRP, 2017). Earlier studies focus primarily on a particular type of extreme, such as heatwaves, extreme precipitation events, droughts and floods (Diffenbaugh & Ashfaq, 2010; Singh et al., 2013; Wehner et al., 2011; Wobus et al., 2017). More recently, research on “compound extremes” – such as the simultaneous occurrence of droughts and heatwaves – has gathered greater attention due to their exacerbated impact on natural and human systems (Mazdiyasni & AghaKouchak, 2015; Zscheischler & Seneviratne, 2017). This emerging understanding of the characteristics of co‐occurring extremes motivates the need to more fully quantify potential exposure to multi‐variate climate extremes, particularly at highly localized spatial scales at which impacts often manifest.

Given the critical importance of local climate conditions in population exposure to aggregate multi‐variate extremes, a key uncertainty is the specific patterns of change for different types of extremes at a given location. For instance, at present, hot and cold extremes are associated with some of the highest mortality rates among climate‐related events (i.e. lightning, storms, floods, etc.) (Thacker et al., 2008). However, cold‐related deaths may exhibit a decline in the coming decade as a result of warmer temperatures (Sheridan & Allen, 2015). A potential decrease in cold‐related extremes could thus strongly influence the population exposure to aggregated climate extremes, particularly over regions where cold extremes pose one of the greatest natural risks for human mortality.

Similarly, nighttime temperatures across the U.S. are warming at a much faster rate than daytime temperatures, which can increase the risk of heat‐related mortality and sleep loss in humans (Murage et al., 2017; Obradovich et al., 2017), effect the reproductive capability and migration timing in birds (Crick, 2004), and reduce yields in agricultural crops (Welch et al., 2010). Therefore, investigations focused on changes in maximum temperature alone cannot provide a complete understanding of variations in exposure to temperature related extremes. Likewise, recent studies demonstrate that some regions may experience an increased frequency in both extreme wet and extreme dry precipitation events (Pagan et al., 2016; Swain et al., 2018). Therefore, an examination of combined impacts of variations in different kinds of climate extremes is necessary to accurately quantify changes in the risk associated with changing exposure to adverse conditions.

Several studies have made use of aggregate metrics to account for spatial heterogeneity in multivariate mean climate change (Diffenbaugh & Giorgi, 2012; Diffenbaugh et al., 2008; Giorgi, 2006). However, similar attempts have not been made to investigate changes in the exposure of natural and human systems to aggregated metrics of multiple types of extremes. Likewise, the “time of emergence” (Giorgi & Bi, 2009), which is the time when future changes permanently exceed the baseline variability, is critical to understand how much of an increase in radiative forcing would lead compound extremes to exceed the levels to which humans and ecosystems have become adapted. While previous studies have calculated the time of emergence of changes in different mean climate quantities (Barrow & Sauchyn, 2019; Hawkins & Sutton, 2012; Lee et al., 2016), the spatial heterogeneity in the time of emergence of change in aggregate multivariate extreme climate has not yet been quantified.

This study quantifies changes in the spatial pattern of multivariate extreme climate using the Climate Extremes Index (CEI), a multi‐extreme index developed by the U.S. National Center for Environmental Information (NCEI) (Gleason et al., 2008). The CEI combines various measures of hot, cold, wet and dry extremes (see Methods for a complete list of variables) and is currently monitored at seasonal and annual timescales for the U.S. and nine climate regions. While the CEI does not include some key extreme event types (such as storm surges and wildfires), it nonetheless provides a relatively comprehensive metric to quantify concurrent multi‐variate extremes, thereby enabling the analysis of overall exposure to extreme climate conditions.

There are a number of well‐documented barriers to the reliable estimation of population exposure to changes in climate extremes at regional‐ to local‐scales, including the heterogeneity in social‐ecological systems and the uncertainty in the projections of General Circulation Models (GCMs) (Giorgi, 2014; Giorgi et al., 2008). The complexities within social‐ecological systems, which are a combination of interacting social and ecological subsystems, result in a spatially heterogenous distribution of the human population (Liu et al., 2007). Similarly, future projections of GCMs lack the necessary spatial details in the climate change signal needed for the fine‐scale assessment of climate change impacts (Giorgi et al., 2008; Giorgi et al., 2009). Therefore, a robust and highly resolved estimation of population exposure to multi‐variate extremes not only requires a climate model ensemble that captures both the uncertainty in the large‐scale climate reflected in multi‐GCM ensembles and fine‐scale climate processes that are critical for regulating the response of regional‐ and local‐scale climate to global warming, but it also requires spatially detailed estimates of current and future population distributions. To address these needs, this study employs the high‐resolution climate model ensemble described in Ashfaq et al. and Naz et al. (Ashfaq et al., 2016; Naz et al., 2016), current population estimates provided in the Gridded Population of the World version 4 (GPWv4) data (CIESIN, 2018), and the future period population projections based on the Shared Socio‐Economic Pathways (SSP; (Jones & O’Neill, 2016). The climate model ensemble, which consists of a regional climate model nested within multiple GCMs in the 5th phase of the Coupled Model Intercomparison Project (CMIP5), encompasses one of the most detailed regional climate projections generated over the full contiguous U.S. Relative to the driving GCMs, the ensemble exhibits substantial improvements in the simulation of spatial and temporal characteristics of the regional hydro‐climate (Ashfaq et al., 2016; Naz et al., 2016). The GPWv4 and SSP datasets provide population distributions at a 1‐km horizontal grid spacing. Together, the use of highly resolved climate change projections and population distributions enables the investigation of changes in the exposure to individual and aggregate extremes at unprecedented spatial scales.