A humid heat extreme (HHE) is a type of compound extreme where heat co-occurs with high humidity levels. It poses a greater risk to human health than heat extremes with low humidity because humidity adversely affects the body’s cooling mechanism, making sweating and evaporation less efficient. Therefore, the heat index, which accounts for both temperature and relative humidity, is often used to define a humid heat extreme.

Existing HHE forecast tools, including the Centers for Disease Control and Prevention (CDC) HeatRisk Forecast Tool and the National Weather Service HeatRisk tool, focus on short-term weather scales (e.g., up to 7 days). There is a need to develop an early warning system for forecasting HHE on longer time scales, which motivates our study on predicting HHE on seasonal scales. While short-term forecasts for HHE are relatively easier with the help of numerical models, predicting HHE beyond the weather scale is more challenging. However, long-term seasonal forecasts can be achieved by examining the frequency of HHE occurrences over a season, rather than forecasting individual events. Our goal is to determine whether year-to-year variations in the frequency of summertime (June-August, i.e., JJA) HHE are predictable.

Using GFDL’s (Geophysical Fluid Dynamics Laboratory) Seamless System for Prediction and Earth System Research (SPEAR) — a coupled ocean-atmosphere seasonal forecast model — our study demonstrates that the southeastern United States (SEUS) is a hotspot for summertime HHE in the United States (Figure 1). The frequency of summertime HHE in the SEUS can be skillfully predicted one month in advance (Figure 2). Further analysis from observations and model simulations reveals that sea surface temperatures (SSTs) in the tropical North Atlantic are the primary source of this predictability. Warmer-than-normal SSTs in the tropical North Atlantic create large-scale conditions that favor the transport and retention of heat and moisture to the SEUS. 

Figure 1. The climatological frequency of summertime (i.e., JJA) HHE in a ERA5 reanalysis, b SPEAR hindcasts, and c the bias. The SPEAR hindcasts are initialized on June 1st, referred to as lead 0-month seasonal hindcasts of the JJA season. The climatological base period is from 1995 to 2022. The black box in a indicates the high-frequency area that is averaged in the assessment of the prediction skill of HHE.

Figure 2. Correlation skill of the frequency of HHE in SEUS. a Time series of the frequency of HHE in the JJA season averaged over SEUS from ERA5 reanalysis (black), SPEAR hindcasts initialized on June 1st (red) and May 1st (dark red), SPEAR historical simulations (blue), AMIP (green) and AMIP-TNA (magenta) simulations. b Same as (a), but for the linearly-detrended time series. The correlation coefficients between the frequency of HHE in the ERA5 and model simulations are labeled in each panel. The asterisk indicates the correlation coefficient is significant at the 5% level.

However, several questions remain. Are there other possible secondary sources of predictability for summertime HHE in SEUS? Although the model represents HHE and its mechanisms reasonably well, there are still discrepancies between model simulations and observations. The potential causes of these discrepancies deserve attention. Nevertheless, our findings shed light on the predictability of high-impact compound extremes on time scales beyond weather forecasting. The results of this study have potential applications in the development of early warning systems for HHE.