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More frequent and intensified high‐temperature extremes are acceptable indicators of global warming, which pose serious socio‐economic impacts. In the present research, the Climate Prediction Center daily minimum and maximum temperature are used to characterize high temperatures into intensity of hot days (TXx), hot nights (TNx), and frequency indices based on the 90th percentile of hot days (TX90p) and hot nights (TN90p) as defined by the Expert Team on Climate Change Detection and Indices from 1981 to 2020 over Africa. The regression approach based on iterative reweighted least squares and correlation analyses are used to estimate the trends in extreme high‐temperature events and their relationship with various meteorological variables. Furthermore, the Pruned exact linear time algorithm is used to assess if the maximum temperature experienced an abrupt change. Results show that many parts …