Current evaluation metrics for deep learning weather models create a "Statistical Similarity Trap", rewarding blurry predictions while missing rare, high-impact events. We provide quantitative evidence of this trap, showing sophisticated baselines achieve 97.9% correlation yet 0.00 CSI for dangerous convection detection. We introduce DART (Dual Architecture for Regression Tasks), a framework addressing the challenge of transforming coarse atmospheric forecasts into high-resolution satellite brightness temperature fields optimized for extreme convection detection (below 220 K). DART employs dual-decoder architecture with explicit background/extreme decomposition, physically motivated oversampling, and task-specific loss functions. We present four key findings: (1) empirical validation of the Statistical Similarity Trap across multiple sophisticated baselines; (2) the "IVT Paradox", removing Integrated Water Vapor Transport, widely regarded as essential for atmospheric river analysis, improves extreme convection detection by 270%; (3) architectural necessity demonstrated through operational flexibility (DART achieves CSI = 0.273 with bias = 2.52 vs. 6.72 for baselines at equivalent CSI), and (4) real-world validation with the August 2023 Chittagong flooding disaster as a case study. To our knowledge, this is the first work to systematically address this hybrid conversion-segmentation-downscaling task, with no direct prior benchmarks identified in existing literature. Our validation against diverse statistical and deep learning baselines sufficiently demonstrates DART's specialized design. The framework enables precise operational calibration through beta-tuning, trains in under 10 minutes on standard hardware, and integrates seamlessly with existing meteorological workflows, demonstrating a pathway toward trustworthy AI for extreme weather preparedness.

翻译：当前深度学习天气模型的评估指标存在"统计相似性陷阱"，即奖励模糊预测却遗漏罕见的高影响事件。我们提供了该陷阱的定量证据，表明复杂基线模型在危险对流检测中实现了97.9%的相关性，但临界成功指数(CSI)为0.00。我们提出DART（回归任务双架构）框架，以解决将粗分辨率大气预报转化为针对极端对流检测（低于220 K）优化的高分辨率卫星亮温场的挑战。DART采用具有显式背景/极端分解的双解码器架构、物理驱动的过采样技术和任务特定损失函数。我们呈现四项关键发现：(1) 在多个复杂基线模型中实证验证统计相似性陷阱；(2) "IVT悖论"：移除被广泛认为对大气河分析至关重要的积分水汽输送，使极端对流检测性能提升270%；(3) 通过操作灵活性证明架构必要性（DART在同等CSI水平下实现CSI=0.273且偏差=2.52，而基线模型偏差为6.72）；(4) 以2023年8月吉大港洪灾为案例进行实际验证。据我们所知，这是首个系统解决这种混合转换-分割-降尺度任务的研究，现有文献中未发现直接先验基准。我们通过对比多种统计与深度学习基线的验证，充分证明了DART的专项设计优势。该框架支持通过β调谐实现精确的业务化校准，在标准硬件上训练时间不足10分钟，并能无缝集成到现有气象业务流中，为构建可信赖的极端天气预警人工智能开辟了可行路径。