Compound drought and heatwave (CDHW) events have garnered increased attention due to their significant impacts on agriculture, energy, water resources, and ecosystems. We quantify the projected future shifts in CDHW characteristics (such as frequency, duration, and severity) due to continued anthropogenic warming relative to the baseline recent observed period (1982 to 2019). We combine weekly drought and heatwave information for 26 climate divisions across the globe, employing historical and projected model output from eight Coupled Model Intercomparison Project 6 GCMs and three Shared Socioeconomic Pathways. Statistically significant trends are revealed in the CDHW characteristics for both recent observed and model simulated future period (2020 to 2099). East Africa, North Australia, East North America, Central Asia, Central Europe, and Southeastern South America show the greatest increase in frequency through the late 21st century. The Southern Hemisphere displays a greater projected increase in CDHW occurrence, while the Northern Hemisphere displays a greater increase in CDHW severity. Regional warmings play a significant role in CDHW changes in most regions. These findings have implications for minimizing the impacts of extreme events and developing adaptation and mitigation policies to cope with increased risk on water, energy, and food sectors in critical geographical regions.

The 2022 Compound Drought and Heatwave (CDHW) caused widespread crop damage, water shortages, and wildfires across Europe. Our study analyzed this event’s severity and return period (RP) and compared it with past mega CDHWs in Europe. The hardest‐hit areas were Iberian Peninsula, France, and Italy, where temperatures exceeded 2.5°C above normal, and severe droughts persisted from May to August. Using a Bayesian approach, we estimated the RP for the 2022 CDHW event, which was unprecedented in Northern Italy, Iberian Peninsula, and western parts of France, with RPs of 354, 420, and 280 years, respectively. The reduced soil moisture due to precipitation deficits and high temperatures contributed to the persistence and severity of drought, creating a positive feedback loop where dry soils led to even drier conditions. In light of our findings, it is evident that global warming poses increased risks of severe CDHW events, which are likely to increase.

<p>The Intensity-Duration-Frequency (IDF) relationships are commonly used in urban hydrologic designs. A major source of uncertainty arises due to small samples of data and use of multiple GCMs, in developing the IDF for future periods. A major objective of this study is to address uncertainties in the IDF relationships for future periods, under climate change. The study proposes a Bayesian method for addressing the parameter uncertainty in the Generalized Extreme Value (GEV) distribution for the Annual Maximum Series (AMS). Uncertainties due to the use of multiple GCMs are addressed through the Reliable Ensemble Averaging (REA) method. The posterior distributions of the three parameters of GEV distribution are obtained using Markov Chain Monte Carlo (MCMC) method. Twenty-three CMIP5 GCMs with four RCPs are considered for studying the effect of climate change on the IDF relationship for the case study of Bangalore, India. Change Factor Method (CFM) is used for spatially downscaling the projected time series of precipitation and scale-invariance theory is used for temporal disaggregation. Results are compared across different CFM schemes considering multiple bin sizes. Uncertainties in design intensities are quantified through probabilistic IDF relationships.</p>