用多分位数回归提升极端降水模拟，显著增强洪水风险预警能力。
Multi-Quantile Regression for Extreme Precipitation Downscaling
- 采用分位数损失函数，分别训练不同极端水平的预测通道。
- 在佛罗里达州，极端事件检测率提升至75.7%，是基线的18倍。
- 适合关注极端天气建模、气候风险评估的研究者使用。
深度超分辨率网络在降水降尺度中表现良好，但对驱动洪水风险的极端尾部事件存在系统性低估。我们发现根本问题在于损失函数而非数据：强度加权MAE会使相同输入下的真实与合成标签平均化，导致数据增强仅改变均值而非条件分布。为此提出Q-SRDRN，一种基于pinball损失在0.50、0.95、0.99、0.999分位数上训练的多分位数超分辨率网络。两个特定设计使其实用化：IncrementBound保持单调性并保留各分位数通道的梯度一致性；独立输出头为整体和尾部检测提供分离的滤波器组。在此设计下，cVAE生成样本成为互补：中位数头吸收合成模式而不污染高分位数。实证显示，在佛罗里达（对流/热带气旋主导），未使用增强的Q-SRDRN P999头在200 mm/day阈值下检测到1,598/2,111个事件，相比基线的88例提升18倍（4.2%→75.7%），KL散度降低63%，RMSE下降3.9%。加入cVAE后P50通道命中数从14增至1,038。在加州（大气河主导），该架构在300 mm/day以下实现近乎完美检测（P999 SEDI ≥ 0.996）。德克萨斯州基线仅捕获10,720事件中的2例，而P999头捕获8,776例（81.9%）。尽管cVAE不跨区域迁移，但多分位数回归可在大尺度信号强处捕捉极端事件，而增强则补足信号弱处的中位数预测。
原文摘要 · Abstract (English)
Deep super-resolution networks for precipitation downscaling achieve strong bulk skill yet systematically under-predict the heavy-tail events that drive flood risk. We demonstrate that the primary obstacle is the loss function, not the data: under intensity-weighted MAE, real and synthetic labels at the same input are simply averaged, meaning data augmentation shifts the predicted mean rather than the conditional distribution. We resolve this with Q-SRDRN, a multi-quantile super-resolution network trained with pinball loss at tau in 0.50, 0.95, 0.99, 0.999. Two CNN-specific design choices make this practical: IncrementBound enforces monotonicity while preserving each quantile channel's gradient identity, and separate per-quantile output heads provide independent filter banks for bulk and tail detection. Under this design, data augmentation via cVAE becomes complementary: the median head absorbs synthetic patterns without contaminating upper quantiles. Empirically, on Florida (convective/tropical-cyclone dominated), the un-augmented Q-SRDRN P999 head detects 1,598 of 2,111 events at 200 mm/day versus 88 for the deterministic baseline--an 18x detection-rate gain (4.2% to 75.7%)--with 63% lower KL divergence and 3.9% lower RMSE. Adding cVAE-generated samples lifts the P50 channel from 14 to 1,038 hits at 200 mm/day. On California (atmospheric-river dominated), the architecture reaches near-perfect detection (P999 SEDI >= 0.996 through 300 mm/day). On Texas, the baseline catches only 2 of 10,720 events at 200 mm/day while the P999 head catches 8,776 (81.9%). While the cVAE does not transfer across regions, multi-quantile regression captures extremes wherever the large-scale signal is strong, while augmentation rescues the median where it is not.
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