| Summary: | The occurrence of very high temperatures (hot extremes) is often linked with negative impacts in human health, natural ecosystems and the economy (e.g., energy, water supply and agriculture). Studies have invariably shown that the intensity and frequency of hot extremes will increase in the future thus increasing their associated risks. While much progress has been made in quantifying and understanding hot temperature extremes and their future changes, there are still open questions. This paper focusses on the sources of hot extremes and their changes by applying a simple and unambiguous methodology that describes daily hot extremes as the superposition of four well known physical terms that include information on the annual mean temperature, the amplitude of the annual cycle, the diurnal temperature range and the local temperature anomaly on the day of the extreme. The methodology was applied to 30-year daily temperature records from 6 observation-based datasets and 31 atmosphere-ocean global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The comparison between observed and simulated hot extremes shows a remarkably consistent picture where most CMIP5 models overestimate the term describing the local temperature extreme anomaly over most regions of the globe regardless of the observed dataset considered. Simultaneously, CMIP5 models show a systematic cold bias in the annual mean temperature and in the diurnal temperature range terms leading to substantial error compensation over some regions. This prompted us to define a new error estimator as the sum of errors in individual terms that appears to be much more effective at characterising model's performance compared to the traditional bias estimator. The assessment of future changes in hot extremes shows that changes are dominated by changes in annual mean temperatures with varying contributions from the other terms that strongly depend on the specific region being considered. Western Europe appears as a hot spot for extreme temperature changes (increases of ~8 ∘C by the end of the century) due to significant contributions from all decomposition terms including the summer mean anomaly, the diurnal temperature range and the daily extreme anomaly. Tropical South America also appears as a hot spot for extreme temperature changes (increases of ~7 ∘C) largely due to an increase in the daily extreme anomaly term (explaining about 30% of the full change) making this region one of the most sensitive regions in the world in terms of hot extremes. The analysis reveals that the separation of future changes according to terms describing mean, variability and tails is very sensitive to the specific way the mean component is defined including assumptions about stationarity.
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