Can wet heatwaves be represented by CMIP6 models and bias-corrected NEX-GDDP-CMIP6?

Abstract

As global warming intensifies, wet heatwaves pose an increasing threat to human health. As global climate models (GCMs) and their bias-corrected datasets are commonly used in wet heatwave research, it is essential to determine whether these datasets accurately represent wet heatwaves. We conducted a global assessment of 32 GCMs from CMIP6 and the NEX-GDDP-CMIP6, simulating wet heatwaves based on extended summer wet-bulb temperature (T_w) during 1981–2014, and compared them with the Global Meteorological Forcing Dataset (GMFD). Our findings indicate CMIP6 overestimates mean wet heatwave number (HWN), frequency (HWF), and duration (HWD), with relative biases from −32 % to 96 % (global mean: 10 %), −15 %–81 % (mean: 35 %), and −12 %–50 % (mean: 25 %) across IPCC climate reference regions, respectively. The exaggerated temporal autocorrelation of T_w, which statistically reflects an excessively persistent state of abnormal humid heat in GCMs, inflating biases in the count of events with long duration that probably lead to the overestimations of HWN, HWF, and HWD. CMIP6 significantly underestimates wet heatwave magnitude (HWM) by −70 %–6 % (mean: 9 %), primarily due to its underestimation of extreme T_w values. While the NEX-GDDP-CMIP6 dataset reduces this bias through the application of the quantile mapping method, it has limited effectiveness in correcting T_w autocorrelation, which restricts improvements in metrics such as HWN, HWF, and HWD. We find that heatwave intensity (HWI), reflecting the cumulative impact of heat, is influenced by the interplay between HWM and HWD. We identify the best-performing models for simulating wet heatwaves across continents using NEX-GDDP-CMIP6. These findings highlight the need for careful evaluation of both raw and bias-corrected datasets before using them in climate risk assessments.