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<p>This study examines the variability, trends, and projected changes in seasonal extreme temperature indices across Rwanda. Gridded daily temperature data (5&#xa0;km resolution) for 1983–2021 from the Rwanda Meteorology Agency were analyzed alongside projections from the CORDEX-CORE multi-model ensemble for the near future (2026–2060) and far future (2066–2100) under RCP2.6 and RCP8.5 scenarios, relative to the observation period (1986–2020). Five indices were computed following the Expert Team on Climate Change Detection and Indices (ETCCDI) methodology. They include: i) diurnal temperature range (DTR); ii) cold nights (Tn10p); iii) warm nights (Tn90p); iv) cold days (Tx10p); and v) warm days (Tx90p) for January–February (JF), March–April-May (MAM), June-July–August (JJA) and September–October-November–December (SOND). Results indicate more variability during the observation period in Tn90p for MAM ([1.7, 4.5] days), JJA ([1.4, 5.2] days) and SOND ([1.6, 4.8] days) and low variability for DTR across seasons ([0.4, 2.0] °C). The trends show spatial heterogeneity, with both increasing and decreasing patterns across regions. DTR is projected to rise in the south-eastern and north-eastern regions but decline in the south-central, south-western, and north-western areas for both future periods. Tn10p is expected to decrease under RCP2.6 and increase under RCP8.5 in the near future, with reversed patterns in the far future, while Tn90p is projected to increase more under RCP8.5 compared to RCP2.6 for both timeframes. Tx10p generally increases in the near future but decreases later for JF, JJA and SOND, while reducing countrywide in MAM, whereas Tx90p exhibits mixed regional changes across seasons under both emission scenarios. These findings highlight increasing temperature extremes and spatial contrasts in Rwanda, underscoring the need for targeted adaptation strategies in climate-sensitive sectors such as agriculture, health, and tourism.</p>

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Observed and future projections of seasonal extreme temperature indices in Rwanda

  • 期刊论文

  • 发表日期：

    2025年12月6日

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  • Ndakize Joseph Sebaziga,
  • Bonfils Safari,
  • Joshua Ndiwa Ngaina,
  • Didier Ntwali

摘要

This study examines the variability, trends, and projected changes in seasonal extreme temperature indices across Rwanda. Gridded daily temperature data (5 km resolution) for 1983–2021 from the Rwanda Meteorology Agency were analyzed alongside projections from the CORDEX-CORE multi-model ensemble for the near future (2026–2060) and far future (2066–2100) under RCP2.6 and RCP8.5 scenarios, relative to the observation period (1986–2020). Five indices were computed following the Expert Team on Climate Change Detection and Indices (ETCCDI) methodology. They include: i) diurnal temperature range (DTR); ii) cold nights (Tn10p); iii) warm nights (Tn90p); iv) cold days (Tx10p); and v) warm days (Tx90p) for January–February (JF), March–April-May (MAM), June-July–August (JJA) and September–October-November–December (SOND). Results indicate more variability during the observation period in Tn90p for MAM ([1.7, 4.5] days), JJA ([1.4, 5.2] days) and SOND ([1.6, 4.8] days) and low variability for DTR across seasons ([0.4, 2.0] °C). The trends show spatial heterogeneity, with both increasing and decreasing patterns across regions. DTR is projected to rise in the south-eastern and north-eastern regions but decline in the south-central, south-western, and north-western areas for both future periods. Tn10p is expected to decrease under RCP2.6 and increase under RCP8.5 in the near future, with reversed patterns in the far future, while Tn90p is projected to increase more under RCP8.5 compared to RCP2.6 for both timeframes. Tx10p generally increases in the near future but decreases later for JF, JJA and SOND, while reducing countrywide in MAM, whereas Tx90p exhibits mixed regional changes across seasons under both emission scenarios. These findings highlight increasing temperature extremes and spatial contrasts in Rwanda, underscoring the need for targeted adaptation strategies in climate-sensitive sectors such as agriculture, health, and tourism.