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 90^th percentile of hot days (TX90p) and hot nights (TN90p) as defined by the Expert Team on Climate Change Detection Indices (ETCCDI) 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 of Africa experienced more frequent and intensified hot days and nights in the current period (1998-2020) compared to the recent past (1981-1997), suggesting a clear shift in climate. Thus, we used the climatological mean differences between meteorological parameters before and after the breakpoint to assess the relationship between atmospheric conditions and extreme high-temperature events. As a result, we found that the current period observed an increase in the geopotential height at 500 hPa and reduced total cloud cover, as well as an increase in upward longwave radiation resulting in an upsurge in the frequency of hot days over many African Sub-regions. The present study's findings provide useful information for future planning and development of early warning systems to cope with risks associated with hot extremes.

Following the December 2015 Paris Agreement, there is an international effort to limit global warming at 1.5^oC and 2.0^oC relative to the pre‐industrial level. However, there is inadequate research quantifying the difference in extreme temperature between the two target levels, especially in Africa. This study used historical and future low‐warming projections of NCAR‐CESM to assess the differences in extreme temperature events under 1.5^oC and 2.0^oC future scenarios relative to the recent climate taken from 1976‐2005 over Africa. The results show that compared with the present day, the magnitude of the highest temperature (TXx) will increase by approximately 1.6^oC/2.2^oC in 1.5^oC/2.0^oC warming worlds, with more substantial warming in Sahara (SAH) and Southern Africa (SAF). The lowest temperature (TNn) will increase by ~1.9^oC/2.5^oC in 1.5^oC/2.0^oC future climates, where SAH and SAF will experience a higher intensity of warming followed by Eastern Africa (EAF) and Western Africa (WAF) regions. The frequency of hot days (TX90p) is projected to increase with global warming across the continent, with a high increase in SAF and SAH. The frequency of cold days (TN10p) is expected to decrease with global warming in many parts of Africa except in WAF. In most regions, there were no apparent changes revealed in the Warm Spell Duration Indicator (WSDI) relative to the recent climate across the continent except SAH in 1.5^oC and 2.0^oC warming levels, while a decrease in Cold Spell Duration Indicator (CSDI) will be in SAH. Assessment of the avoided impacts in 1.5^oC relative to 2.0^oC indicates that limiting global warming at 1.5^oC rather than 2.0^oC could reduce the intensity, frequency, and duration of extreme temperatures from 3‐81%. However, the magnitude of avoided impacts differs among indices and sub‐regions. Hence, it is paramount for the African continent to limit global warming at 1.5^oC instead of 2.0^oC.

The paths of winter cold surge (CS) events in East Asia (EA) from 1979 to 2017 are tracked by the Flexible Particle (FLEXPART) model using ERA-Interim daily datasets, and the probability density distribution of the paths is calculated by the kernel density estimation (KDE) method. The results showed that the paths of CSs are significantly correlated with the intensity of the CSs, which shows an interdecadal transition from weak to strong around 1995. CS paths can be classified into two types, namely, the western path type and the northern path type, which were more likely to occur before and after 1995, respectively. Before 1995, the cold air mainly originated from Europe and moved from west to east, and the synoptic features were associated with the zonal wave train. After 1995, cold air accumulated over western Siberia and then invaded EA along the northern path, and the synoptic features were mainly associated with the blocking structure. The geopotential height (GPH) anomalies over the Arctic were abnormally strong. This paper further analyzes the relationship between CSs and winter sea ice concentration (SIC) in the Arctic. The results show that the intensity of CSs is negatively correlated with the Barents SIC (BSIC). When the BSIC declines, the upward wave flux over the Barents Sea is enhanced and expanded to the midlatitude region. GPH anomalies over the Arctic are positive and form a negative AO-like pattern, which is conducive to the formation of the northern path CS. Furthermore, the observed results are supported by numerical experiments with the NCAR Community Atmosphere Model, version 5.3 (CAM5.3).