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<p>Extreme temperature events pose substantial risks to both natural environments and communities across Central Africa (CA). Gaining deeper insight into how these events vary is crucial to inform effective climate change mitigation and adaptation plans. The present study evaluates the suitability of global climate models from the Phase 6 of the Coupled Model Intercomparison Project (CMIP6), along with their multi-model ensemble mean (MME), in simulating the recent past spatial characteristics of extreme temperature events in CA. For this purpose, under the period 1985–2014, we assessed five relevant indicators based on daily minimum and maximum temperatures, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). We examined the spatial patterns of these extreme temperature indices as simulated by sixteen CMIP6 models and their MME against CHIRTS and ERA5 observational and reanalysis datasets. Our focus was on percentile, absolute, and duration-based indices. We found that, the MME generally showed better agreement with observations, though its skill often masks systemic deficiencies, particularly for duration-based indices (WSDI and CSDI) and across the equatorial sub-regions (EQE, EQW, SE). Our findings critically guide CMIP6 model selection for climate impact studies by demonstrating that the MME's successful performance is often due to compensating errors. This study provides policymakers with a better understanding of the uncertainties, particularly the misrepresentation of warm and cold spell durations, essential for robust regional adaptation and mitigation strategies.</p>

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An evaluation of extreme temperature indices as simulated by CMIP6 models over Central Africa

  • 期刊论文

  • 发表日期：

    2026年3月5日

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  • Gabriel Fotso-Kamga,
  • Thierry C. Fotso-Nguemo,
  • Zéphirin D. Yepdo,
  • Alain T. Tamoffo,
  • Zakariahou Ngavom,
  • Sinclaire Zebaze,
  • Cyrille K. Kayimbo,
  • Derbetini A. Vondou,
  • Arona Diedhiou

摘要

Extreme temperature events pose substantial risks to both natural environments and communities across Central Africa (CA). Gaining deeper insight into how these events vary is crucial to inform effective climate change mitigation and adaptation plans. The present study evaluates the suitability of global climate models from the Phase 6 of the Coupled Model Intercomparison Project (CMIP6), along with their multi-model ensemble mean (MME), in simulating the recent past spatial characteristics of extreme temperature events in CA. For this purpose, under the period 1985–2014, we assessed five relevant indicators based on daily minimum and maximum temperatures, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). We examined the spatial patterns of these extreme temperature indices as simulated by sixteen CMIP6 models and their MME against CHIRTS and ERA5 observational and reanalysis datasets. Our focus was on percentile, absolute, and duration-based indices. We found that, the MME generally showed better agreement with observations, though its skill often masks systemic deficiencies, particularly for duration-based indices (WSDI and CSDI) and across the equatorial sub-regions (EQE, EQW, SE). Our findings critically guide CMIP6 model selection for climate impact studies by demonstrating that the MME's successful performance is often due to compensating errors. This study provides policymakers with a better understanding of the uncertainties, particularly the misrepresentation of warm and cold spell durations, essential for robust regional adaptation and mitigation strategies.