Nowadays, extreme temperatures are associated with global warming and cause great concerns worldwide, particularly in Africa because of low resilience, adaptation and mitigation strategies. The present study investigates the performance of RegCM4 climate model in reproducing extreme temperatures over Central Africa (CA) during 2002–2006 period. Apart of daily minimum and maximum temperatures, the study uses six extreme temperature indices based on the recommendations of Expert Team on Climate Change Detection and Indices. These include four intensity indices and two duration indices. Focus has been done in two sub-regions embedded over the study area for more specific analysis. The first sub-region named zone 1 is semi-arid while the second one named zone 2 is humid. We found that whatever the region or season over CA, RegCM4 displays fairly well the intensity indices associated to daily minimum temperatures, namely \(TN_{N}\) and \(TN_{X}\), with underestimations not exceeding \(4\,^{\circ }\)C. For intensity indices based on daily maximum temperatures, namely \(TX_{X}\) and \(TX_{N}\), the warm bias can reach up to \(10\,^{\circ }\)C. This model behavior is associated to its inability to depict surface downward solar and thermal fluxes as well as surface sensible heat flux. Regarding the duration indices, RegCM depicts the hot sequences with high biases, unlike the cold sequences. Statistical parameters used for evaluation show that despite the differences between model and ERA5, RegCM4 can be used to investigate extreme temperatures over the study region mainly if indices of interest are based on daily minimum temperatures and cold sequences.

如今，极端温度与全球变暖有关，并在世界范围内引起极大关注，特别是在非洲，因为其复原力、适应和缓解策略低。本研究调查了 RegCM4 气候模型在 2002-2006 年期间再现中非（CA）极端温度的性能。除了每日最低和最高温度外，该研究还根据气候变化检测和指数专家组的建议使用了六个极端温度指数。其中包括四个强度指数和两个持续时间指数。重点已在嵌入研究区域的两个子区域进行，以便进行更具体的分析。第一个名为区域 1 的子区域是半干旱的，而名为区域 2 的第二个子区域是潮湿的。我们发现无论加利福尼亚州的地区或季节如何，\(TN_{N}\)和\(TN_{X}\)，低估不超过\(4\,^{\circ }\) C. 对于基于每日最高温度的强度指数，即\(TX_{ X}\)和\(TX_{N}\)，暖偏差可以达到\(10\,^{\circ }\)C. 该模型行为与其无法描绘地表向下的太阳和热通量以及地表感热通量有关。关于持续时间指数，RegCM 描述了具有高偏差的热序列，与冷序列不同。用于评估的统计参数表明，尽管模型和 ERA5 之间存在差异，但 RegCM4 可用于研究研究区域的极端温度，主要是如果感兴趣的指数基于每日最低温度和寒冷序列。