Emergence of climate change signal in CMIP6 extreme indices

Climate and weather extremes are becoming more and more frequent due to the influence of anthropogenic climate change and knowing when and where we can expect these effects to occur is essential for both climate change mitigation and developing adaptation measures. We investigate the time of emergence – meaning the earliest time at which the climate change signal can be detected from the noise of natural variability – for 27 climate extreme indices related to surface temperature and precipitation. An ensemble of 21 CMIP6 global climate models (including several with a large number of initializations) is combined with a model weighting scheme that accounts for both model performance and independence to provide robust ensemble statistics of the emergence of climate extremes and to explore model uncertainty. The results from this comprehensive study indicate that spatial and temporal emergence patterns differ between temperature indices related to absolute values and percentiles, annual maxima and minima indices, and also between seasons for individual indices. Precipitation indices tend to emerge much later and mostly only under high emissions scenarios. The main regions where precipitation emergence occurs are the northern high latitudes and central Africa, although between-model variability is often quite high.