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<p>Over the past century, the Songhua River Basin (SRB) has warmed faster than the global average, underscoring its vulnerability to climate change. This study projects future precipitation and temperature changes in the SRB using a Weighted Multi-Model Ensemble (WMME) based on the TOPSIS method with CMIP6 GCMs. Firstly, the inherent biases in the GCMs were corrected using the Bias Correction and Spatial Downscaling (BCSD) method. Subsequently, the WMME was employed to analyze future precipitation and temperature for three subperiods: near-term (2030–2049), middle-term (2050–2069), and long-term (2070–2099). The BCSD approach improves CMIP6 GCM simulations of historical precipitation and temperature. The WMME has been shown to effectively capture the geographic distribution of mean annual precipitation and temperature, demonstrating improvements over both the raw and bias-corrected Multi-Model Ensemble (MME). Projections for the future indicate significant increases in precipitation and temperature across the SRB, with mean annual precipitation rising by 5.7% to 26.6% and average temperature increasing by 1.32°C to 5.44°C, depending on the scenario. Projected precipitation exhibits pronounced spatial variability, with relatively larger increases over the eastern and southeastern parts of the SRB, while temperature changes show weaker spatial heterogeneity across the basin. These findings could provide an important reference point for the region in addressing climate change.</p>

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Future changes in precipitation and temperature using cmip6 model based on topsis method: focus on Songhua river basin

  • 期刊论文

  • 发表日期：

    2026年3月12日

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  • Shengyu Chu,
  • Weiguo Zhang,
  • Peng Qi,
  • Fengping Li,
  • Hongze Wang,
  • Xingxiu Li,
  • Hailong Jin

摘要

Over the past century, the Songhua River Basin (SRB) has warmed faster than the global average, underscoring its vulnerability to climate change. This study projects future precipitation and temperature changes in the SRB using a Weighted Multi-Model Ensemble (WMME) based on the TOPSIS method with CMIP6 GCMs. Firstly, the inherent biases in the GCMs were corrected using the Bias Correction and Spatial Downscaling (BCSD) method. Subsequently, the WMME was employed to analyze future precipitation and temperature for three subperiods: near-term (2030–2049), middle-term (2050–2069), and long-term (2070–2099). The BCSD approach improves CMIP6 GCM simulations of historical precipitation and temperature. The WMME has been shown to effectively capture the geographic distribution of mean annual precipitation and temperature, demonstrating improvements over both the raw and bias-corrected Multi-Model Ensemble (MME). Projections for the future indicate significant increases in precipitation and temperature across the SRB, with mean annual precipitation rising by 5.7% to 26.6% and average temperature increasing by 1.32°C to 5.44°C, depending on the scenario. Projected precipitation exhibits pronounced spatial variability, with relatively larger increases over the eastern and southeastern parts of the SRB, while temperature changes show weaker spatial heterogeneity across the basin. These findings could provide an important reference point for the region in addressing climate change.