Hot Spots of Climate Extremes in the Future.

A Regional Extreme Climatic Change Index (RECCI), simultaneously considering the changes in intensity, frequency and interannual variability of three major extreme climatic variables (i.e., precipitation, temperature and wind speed), is constructed to represent regional changes of climate extremes in response to global warming. First, the daily outputs from 13 models in the Coupled Model Intercomparison Project phase 5 project in both historical and future simulations under the Representative Concentration Pathway 8.5 scenario are used to compute the extreme climatic indices. Second, the RECCI is computed on both annual and seasonal time scales during three periods (i.e., 2016–2035, 2046–2065 and 2080–2099) over 26 subregions. Finally, the spatiotemporal change of the RECCI is investigated, and then, the 26 subregions are classified into four categories for each period. The first category with the largest RECCI value is very sensitive to global warming, which is called hot spots of climate extremes. The results show that most hot spots are not time invariant on annual and seasonal time scales with some exceptions. On the annual time scale, the Amazon Basin is the only persistent hot spot in all three periods. For the seasonal time scale in March‐April‐May, the climate extremes in the Amazon Basin always display the strongest responsiveness to global warming, and the Eastern Africa is the only persistent hot spot in June‐July‐August in three periods. Similar results are also found for the other two seasons and periods. In addition, the change in extreme temperature is crucial over the East Asia with change in frequency prominent. Key Points: The majority of the regions in different categories of climate extremes are not time invariant on annual and seasonal time scalesThe Amazon Basin is the only persistent hot spot of climate extremes in 2016–2035, 2046–2065 and 2080–2099The Regional Extreme Climatic Change Index is a robust metric that is not sensitive to the actual models chosen [ABSTRACT FROM AUTHOR]