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Uncertainty in the frequency of compound hot-dry events (fHD) in idealised experiments
(Note that an in-depth interpretation of the figure is provided in the Supplementary Material.) Given a present-day bivariate Gaussian distribution of temperature T and precipitation P with a correlation cor(T, P) of -0.5 (first row), 0 (second row), and 0.5 (third row), shading shows the uncertainty in the future fHD associated with uncertainty in the change of mean temperature (left column) and mean precipitation (right column) at given levels of expected changes in mean temperature (shown on the x-axis) and mean precipitation (y-axis). Magenta isolines show the expected fHD resulting from the expected changes in mean temperature and precipitation (they are the same on right and left columns for a given cor(T, P)). The second axes show changes in units of present-day standard deviations. The closed contour shows the kernel density containing 90% of the multimodel mean projected changes in mean temperature and precipitation in units of relative present-day standard deviations over land grid-points (actual changes in ∘C and mm/day are shown in Extended Data Figure 2b and 7b, respectively). The green line indicates changes of equal magnitude in temperature and precipitation, in units of present-day standard deviations. (Note that the difference in magnitude of uncertainty from temperature (left column) and precipitation (right column) results from the fact that the uncertainty in the change of temperature is relatively large compared to the uncertainty in the change of precipitation).

Uncertainty in the frequency of compound hot-dry events (fHD) in idealised experiments (Note that an in-depth interpretation of the figure is provided in the Supplementary Material.) Given a present-day bivariate Gaussian distribution of temperature T and precipitation P with a correlation cor(T, P) of -0.5 (first row), 0 (second row), and 0.5 (third row), shading shows the uncertainty in the future fHD associated with uncertainty in the change of mean temperature (left column) and mean precipitation (right column) at given levels of expected changes in mean temperature (shown on the x-axis) and mean precipitation (y-axis). Magenta isolines show the expected fHD resulting from the expected changes in mean temperature and precipitation (they are the same on right and left columns for a given cor(T, P)). The second axes show changes in units of present-day standard deviations. The closed contour shows the kernel density containing 90% of the multimodel mean projected changes in mean temperature and precipitation in units of relative present-day standard deviations over land grid-points (actual changes in ∘C and mm/day are shown in Extended Data Figure 2b and 7b, respectively). The green line indicates changes of equal magnitude in temperature and precipitation, in units of present-day standard deviations. (Note that the difference in magnitude of uncertainty from temperature (left column) and precipitation (right column) results from the fact that the uncertainty in the change of temperature is relatively large compared to the uncertainty in the change of precipitation).

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Compound hot–dry events—co-occurring hot and dry extremes—frequently cause damages to human and natural systems, often exceeding separate impacts from heatwaves and droughts. Strong increases in the occurrence of these events are projected with warming, but associated uncertainties remain large and poorly understood. Here, using climate model large...

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Some of the most impactful climate and weather events result from compounding drivers. To robustly assess the current and future risk from such compound events, a better understanding of the associated sources of uncertainty is needed. Internal variability confounds detection and attribution of human-induced climate change and imposes irreducible limits on the accuracy of climate projections. Response uncertainty can lead to divergent projections for many societally important quantities such as precipitation. Combined with unknown future greenhouse gas emissions, these uncertainties can result in a socio-economically paralyzing range of future storylines. Climate model large ensembles are uniquely positioned to assess these uncertainties and are rightfully gaining popularity in compound event research, but they need to be accompanied by rigorous model validation and robust observational constraints to reach their full potential in terms of usefulness for practitioners. This perspective discusses these opportunities and challenges at the example of water resources and provides an outlook on application-oriented compound event research with large ensembles.
... [1][2][3] These extreme events are driven by complex interactions among physical processes and initiated by similar synoptic circulation anomalies, 4,5 and they often co-occur. 6,7 As droughts occur more frequently and temperature warming triggers stronger land-atmosphere feedbacks, 8 compound drought-heatwave (CDHW) events have increased globally, 9,10 including in Asia, 11,12 Europe, 13,14 North America, 15 South America, 16 and Oceania, 17,18 amplifying adverse impacts on socio-ecosystem sustainability and human health. 19,20 SCIENCE FOR SOCIETY Droughts and heatwaves are becoming increasingly common. ...