Projections of Climate Extremes over China Under Carbon Neutrality in Different Scenarios Using the Reliability Ensemble Averaging Method

This study investigated future changes in extreme climate events over China under carbon neutrality using an improved reliability ensemble averaging (REA) method, based on 12 Coupled Model Intercomparison Project Phase 6 (CMIP6) models under the SSP1-1.9 and SSP1-2.6 scenarios. Results indicated that carbon neutrality is reached in 2042/2063 under SSP1-1.9/SSP1-2.6. Relative to 1995–2014, the areal-mean increases in annual mean temperature (Tav), hottest day temperature (TXx), coldest night temperature (TNn), total precipitation (Prcptot), and heavy precipitation (R95p) are 1.27/1.61 °C, 1.31/1.69 °C, 1.45/1.84 °C, 9.5/13.3%, and 20.8/29.6% under SSP1-1.9/SSP1-2.6, respectively. Larger increases in Tav and TNn occur in northeastern (NE) and northern China, while those in TXx occur in northwestern (NW) China. Temperature projections are most (least) robust in southeastern China (NE). Prcptot increases largely in the Tibetan Plateau (TB) and NE, while R95p increases largely in TB and south of the Yangtze River. The smallest but most robust precipitation increase is in southwestern China, while the least robust is in NW. The areal-mean Tav, TXx, and TNn increase by 0.34 °C, 0.38 °C, and 0.39 °C under SSP1-2.6 (late carbon neutrality) compared with SSP1-1.9 (early carbon neutrality), while those for Prcptot and R95p increase by 3.8% and 8.8%, respectively. Achieving carbon neutrality earlier may contribute to mitigating future climate change and reducing the worsening of extreme temperature and precipitation extremes over parts of China.

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Publication Details

Journal
Atmosphere
Published
2026-11-02
DOI
https://doi.org/10.3390/atmos17111080
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.00

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article

Projections of Climate Extremes over China Under Carbon Neutrality in Different Scenarios Using the Reliability Ensemble Averaging Method

Huanhuan Zhu, Brian Odhiambo Ayugi
Atmosphere
Climate variability and models
article

Projections of Climate Extremes over China Under Carbon Neutrality in Different Scenarios Using the Reliability Ensemble Averaging Method

Huanhuan Zhu, Brian Odhiambo Ayugi
article en

Abstract

This study investigated future changes in extreme climate events over China under carbon neutrality using an improved reliability ensemble averaging (REA) method, based on 12 Coupled Model Intercomparison Project Phase 6 (CMIP6) models under the SSP1-1.9 and SSP1-2.6 scenarios. Results indicated that carbon neutrality is reached in 2042/2063 under SSP1-1.9/SSP1-2.6. Relative to 1995–2014, the areal-mean increases in annual mean temperature (Tav), hottest day temperature (TXx), coldest night temperature (TNn), total precipitation (Prcptot), and heavy precipitation (R95p) are 1.27/1.61 °C, 1.31/1.69 °C, 1.45/1.84 °C, 9.5/13.3%, and 20.8/29.6% under SSP1-1.9/SSP1-2.6, respectively. Larger increases in Tav and TNn occur in northeastern (NE) and northern China, while those in TXx occur in northwestern (NW) China. Temperature projections are most (least) robust in southeastern China (NE). Prcptot increases largely in the Tibetan Plateau (TB) and NE, while R95p increases largely in TB and south of the Yangtze River. The smallest but most robust precipitation increase is in southwestern China, while the least robust is in NW. The areal-mean Tav, TXx, and TNn increase by 0.34 °C, 0.38 °C, and 0.39 °C under SSP1-2.6 (late carbon neutrality) compared with SSP1-1.9 (early carbon neutrality), while those for Prcptot and R95p increase by 3.8% and 8.8%, respectively. Achieving carbon neutrality earlier may contribute to mitigating future climate change and reducing the worsening of extreme temperature and precipitation extremes over parts of China.

AtmosphereVol. 17(11)
University of Bern (CH), China Meteorological Administration (CN), Wyss Academy for Nature (CH), Wuxi University (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 20%
Climate variability and models
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