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<p><?tk 4?>Climate change is anticipated to increase the frequency and severity of extreme occurrences worldwide. Assessing current and future climate extremes is essential for understanding their impacts and improving adaptation. This study aimed to analyze climate extreme indices using the Coupled Model Inter-comparison Project Phase Six (CMIP6) model under socioeconomic pathways (SSPs) over the Upper Blue Nile Basin, Ethiopia. Observed and projected CMIP6 precipitation and temperature data were analyzed under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. For this study, power transformation and distribution mapping bias correction methods were applied. To assess the trends in extreme temperature and rainfall indices, Sen’s slope and Mann-Kendall (MK) trend test were employed. The study uses a set of 15 standard rainfall and temperature extreme indices as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The result indicated that INM-CM5-0 and INM-CM4-8 models performed best for precipitation and temperature, respectively. Extreme temperature indices revealed that there was a significant decreasing trend in the frequency of cool days (TX10p) and cool nights (TN10p) at <i>p</i> &lt; 0.05 and <i>p</i> &lt; 0.01 significance levels, respectively. The finding also shows a positive and significant (<i>p</i> &lt; 0.001) increasing trend in warm days (TX90p) and warm nights (TN90p). Rainfall indices also showed a significant (<i>p</i> &lt; 0.001) increasing trend in heavy (R10) and very heavy precipitation (R20 and R25) in the study area. The findings suggest that policies should focus on nature-based solutions, such as restoring and protecting natural ecosystems, promoting agroforestry and sustainable land-use practices to enhance resilience against climate change impacts. This could help the community’s capacity to adapt by introducing climate-resilient crop varieties, thereby mitigating the risks associated with the projected increase climate extremes.</p>

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Analyzing the spatiotemporal distribution of climate extremes under the CMIP6 climate model in the upper Blue Nile Basin, Ethiopia

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Fekadie Bazie Enyew,
  • Dejene Sahlu,
  • Gashaw Bimrew Tarekegn,
  • Addis A. Alaminie

摘要

Climate change is anticipated to increase the frequency and severity of extreme occurrences worldwide. Assessing current and future climate extremes is essential for understanding their impacts and improving adaptation. This study aimed to analyze climate extreme indices using the Coupled Model Inter-comparison Project Phase Six (CMIP6) model under socioeconomic pathways (SSPs) over the Upper Blue Nile Basin, Ethiopia. Observed and projected CMIP6 precipitation and temperature data were analyzed under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. For this study, power transformation and distribution mapping bias correction methods were applied. To assess the trends in extreme temperature and rainfall indices, Sen’s slope and Mann-Kendall (MK) trend test were employed. The study uses a set of 15 standard rainfall and temperature extreme indices as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The result indicated that INM-CM5-0 and INM-CM4-8 models performed best for precipitation and temperature, respectively. Extreme temperature indices revealed that there was a significant decreasing trend in the frequency of cool days (TX10p) and cool nights (TN10p) at p < 0.05 and p < 0.01 significance levels, respectively. The finding also shows a positive and significant (p < 0.001) increasing trend in warm days (TX90p) and warm nights (TN90p). Rainfall indices also showed a significant (p < 0.001) increasing trend in heavy (R10) and very heavy precipitation (R20 and R25) in the study area. The findings suggest that policies should focus on nature-based solutions, such as restoring and protecting natural ecosystems, promoting agroforestry and sustainable land-use practices to enhance resilience against climate change impacts. This could help the community’s capacity to adapt by introducing climate-resilient crop varieties, thereby mitigating the risks associated with the projected increase climate extremes.