Projecting Daily Maximum Temperature Using an Enhanced Hybrid Downscaling Approach in Fujian Province, China

Pangpang Gao (), Yuanke Sun, Zhihao Liu, Hejie Zhou and Xiao Li
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Pangpang Gao: School of Environmental Science and Engineering, Xiamen University of Technology, Xiamen 361024, China
Yuanke Sun: School of Environmental Science and Engineering, Xiamen University of Technology, Xiamen 361024, China
Zhihao Liu: School of Environmental Science and Engineering, Xiamen University of Technology, Xiamen 361024, China
Hejie Zhou: School of Environmental Science and Engineering, Xiamen University of Technology, Xiamen 361024, China
Xiao Li: School of Film Television and Communication, Xiamen University of Technology, Xiamen 361024, China

Sustainability, 2025, vol. 17, issue 10, 1-23

Abstract: The rise in global temperatures and increased extreme weather events, such as heatwaves, underscore the need for accurate regional projections of daily maximum temperature (T max ) to inform effective adaptation strategies. This study develops the CNN-BMA-QDM model, which integrates convolutional neural networks (CNNs), Bayesian model averaging (BMA), and quantile delta mapping (QDM) to downscale and project T max under future climate scenarios. The CNN-BMA-QDM model stands out for its ability to capture nonlinear relationships between T max and atmospheric circulation factors, reduce model uncertainty, and correct bias, thus improving simulation accuracy. The CNN-BMA-QDM model is applied to Fujian Province, China, using three CMIP6 GCMs and four shared socioeconomic pathways (SSPs) to project T max from 2015 to 2100. The results show that CNN-BMA-QDM outperforms CNN-BMA, CNNs, and other downscaling methods (e.g., RF, BPNN, SVM, LS-SVM, and SDSM), particularly in simulating extreme value at the 99% and 95% percentiles. Projections of T max indicate consistent warming trends across all SSP scenarios, with spatially averaged warming rates of 0.0077 °C/year for SSP126, 0.0269 °C/year for SSP245, 0.0412 °C/year for SSP370, and 0.0526 °C/year for SSP585. Coastal areas experience the most significant warming, with an increase of 4.62–5.73 °C under SSP585 by 2071–2100, while inland regions show a smaller rise of 3.64–3.67 °C. Monthly projections indicate that December sees the largest increase (5.30 °C under SSP585 by 2071–2100), while July experiences the smallest (2.40 °C). On a seasonal scale, winter experiences the highest warming, reaching 4.88 °C under SSP585, whereas summer shows a more modest rise of 3.10 °C. Notably, the greatest discrepancy in T max rise between the south and north occurs during the summer. These findings emphasize the importance of developing tailored adaptation strategies based on spatial and seasonal variations. The results provide valuable insights for policymakers and contribute to the advancement of regional climate projection research.

Keywords: Bayesian model averaging; climate change projection; convolutional neural networks; daily maximum temperature; downscaling; quantile delta mapping (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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