用深度学习筛选和降尺度32个气候模型，提升欧洲区域气候预测精度。
Regional climate projections using a deep-learning-based model-ranking and downscaling framework: Application to European climate zones
- 基于深度学习的多准则排名，筛选出表现最优的气候模型。
- GeoSTANet降尺度模型在极端温度预测上误差最低，RMSE仅1.57℃。
- 适合做高分辨率气候影响评估与适应规划的研究者使用。
准确的区域气候预测需要对全球气候模型（GCMs）进行高分辨率降尺度。本文提出一种基于深度学习的多模型评估与降尺度框架，利用Deep Learning-TOPSIS（DL-TOPSIS）机制对32个第六次耦合模型比较计划（CMIP6）模型进行排名，并采用先进的深度学习模型优化输出结果。研究涵盖九项性能指标，分析了欧洲五个柯本-季风气候区（热带、干旱、温带、大陆性、极地）在四个季节的表现。尽管TaiESM1和CMCC-CM2-SR5存在明显偏差，但排名显示NorESM2-LM、GISS-E2-1-G和HadGEM3-GC31-LL表现更优。四种模型用于将前几名的GCM降尺度至0.1°分辨率：视觉变压器（ViT）、带有注意力与不平衡感知网络的地理时空变换器（GeoSTANet）、CNN-LSTM和卷积长短期记忆网络（ConvLSTM）。GeoSTANet在捕捉气温极端值（TXx, TNn）方面表现最佳，实现最低误差（均方根误差RMSE=1.57°C，Kling-Gupta效率KGE=0.89，纳什-萨特克利夫效率NSE=0.85，相关系数r=0.92），比ConvLSTM降低20%的RMSE。CNN-LSTM和ConvLSTM在大陆性和温带区域表现良好；而ViT难以捕捉精细尺度温度波动。结果表明，多准则排名能有效提升区域气候研究中模型选择质量，且基于变压器的降尺度方法优于传统深度学习方法。该框架为高分辨率气候预测提供可扩展解决方案，有助于气候影响评估与适应策略制定。
原文摘要 · Abstract (English)
Accurate regional climate forecast calls for high-resolution downscaling of Global Climate Models (GCMs). This work presents a deep-learning-based multi-model evaluation and downscaling framework ranking 32 Coupled Model Intercomparison Project Phase 6 (CMIP6) models using a Deep Learning-TOPSIS (DL-TOPSIS) mechanism and so refines outputs using advanced deep-learning models. Using nine performance criteria, five Köppen-Geiger climate zones -- Tropical, Arid, Temperate, Continental, and Polar -- are investigated over four seasons. While TaiESM1 and CMCC-CM2-SR5 show notable biases, ranking results show that NorESM2-LM, GISS-E2-1-G, and HadGEM3-GC31-LL outperform other models. Four models contribute to downscaling the top-ranked GCMs to 0.1$^{\circ}$ resolution: Vision Transformer (ViT), Geospatial Spatiotemporal Transformer with Attention and Imbalance-Aware Network (GeoSTANet), CNN-LSTM, and CNN-Long Short-Term Memory (ConvLSTM). Effectively capturing temperature extremes (TXx, TNn), GeoSTANet achieves the highest accuracy (Root Mean Square Error (RMSE) = 1.57$^{\circ}$C, Kling-Gupta Efficiency (KGE) = 0.89, Nash-Sutcliffe Efficiency (NSE) = 0.85, Correlation ($r$) = 0.92), so reducing RMSE by 20% over ConvLSTM. CNN-LSTM and ConvLSTM do well in Continental and Temperate zones; ViT finds fine-scale temperature fluctuations difficult. These results confirm that multi-criteria ranking improves GCM selection for regional climate studies and transformer-based downscaling exceeds conventional deep-learning methods. This framework offers a scalable method to enhance high-resolution climate projections, benefiting impact assessments and adaptation plans.
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