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

Pangpang Gao,

Ying Sun, Zhihao Liu

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(10), P. 4360 - 4360

Published: May 12, 2025

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

Language: Английский

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

Pangpang Gao,

Ying Sun, Zhihao Liu

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(10), P. 4360 - 4360

Published: May 12, 2025

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

Language: Английский

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