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<p>Climate change is one of the most extreme challenges of the twenty-first century. Precipitation (pr) and temperature variability are key indicators of climate change detection. Whereas hybrid deep learning (DL) models have been widely applied, their integration with region-specific datasets remains limited. This study uses a CNN-BiLSTM-TCN-attention model to identify the signals of climate change in Thailand based on the 1993–2024 station measurements and CMIP6-GCM simulations. Historical temperature analysis revealed statistically significant warming trends: coastal stations experienced <i>T</i><sub>asmin</sub> increases of up to 0.0673 °C/year and <i>T</i><sub>asmax</sub> of up to 0.0838 °C/year, urban areas 0.0401–0.0733 °C/year, and high-altitude sites 0.02–0.03 °C/year. Precipitation trends were spatially heterogeneous, with increases at Khlong Yai (4.764 mm/year) and Samut Prakan (4.303 mm/year), but declines at Aranyaprathet (− 0.688 mm/year) and Kampaeng Phet (− 0.402 mm/year). After bias correction, the framework achieved high performance (<i>R</i><sup>2</sup> = 0.9807 for <i>T</i><sub>asmin</sub>, 0.9782 for <i>T</i><sub>asmax</sub>, 0.9034 for precipitation) and low error metrics (MSE = 0.0461). Future projections under SSP3-7.0 and SSP5-8.5 indicate widespread emergence of temperature signals, with median detection around 2019 and warming rates of + 0.5 °C/decade; by 2100, <i>T</i><sub>asmin</sub> and <i>T</i><sub>asmax</sub> are projected to rise robustly (+ 0.50 to + 0.72 °C/decade), while precipitation anomalies remain weakly negative (− 0.03 to − 0.08%/decade) with high variability but non-emergent trends. This study’s findings show the importance of applying Thailand-specific observational and model data to effectively detect and quantify local climate change signals, which can be used to inform local adaptation planning.</p>

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Detection of climate change signals using precipitation and temperature time series by a hybrid deep learning framework

  • 期刊论文

  • 发表日期：

    2025年10月21日

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  • Muhammad Waqas,
  • Usa Humphries Wannasingha,
  • Angkool Wangwongchai

摘要

Climate change is one of the most extreme challenges of the twenty-first century. Precipitation (pr) and temperature variability are key indicators of climate change detection. Whereas hybrid deep learning (DL) models have been widely applied, their integration with region-specific datasets remains limited. This study uses a CNN-BiLSTM-TCN-attention model to identify the signals of climate change in Thailand based on the 1993–2024 station measurements and CMIP6-GCM simulations. Historical temperature analysis revealed statistically significant warming trends: coastal stations experienced Tasmin increases of up to 0.0673 °C/year and Tasmax of up to 0.0838 °C/year, urban areas 0.0401–0.0733 °C/year, and high-altitude sites 0.02–0.03 °C/year. Precipitation trends were spatially heterogeneous, with increases at Khlong Yai (4.764 mm/year) and Samut Prakan (4.303 mm/year), but declines at Aranyaprathet (− 0.688 mm/year) and Kampaeng Phet (− 0.402 mm/year). After bias correction, the framework achieved high performance (R2 = 0.9807 for Tasmin, 0.9782 for Tasmax, 0.9034 for precipitation) and low error metrics (MSE = 0.0461). Future projections under SSP3-7.0 and SSP5-8.5 indicate widespread emergence of temperature signals, with median detection around 2019 and warming rates of + 0.5 °C/decade; by 2100, Tasmin and Tasmax are projected to rise robustly (+ 0.50 to + 0.72 °C/decade), while precipitation anomalies remain weakly negative (− 0.03 to − 0.08%/decade) with high variability but non-emergent trends. This study’s findings show the importance of applying Thailand-specific observational and model data to effectively detect and quantify local climate change signals, which can be used to inform local adaptation planning.