Reliable nowcasting of extreme precipitation remains difficult because convective systems are strongly nonlinear, multiscale, and nonstationary in 3D. Radar is the backbone of nowcasting, yet existing methods struggle to predict extremes: physics-based extrapolation cannot capture growth and decay, deterministic learning tends to oversmooth and underestimate peaks, and purely generative models often lack physical consistency. Hybrid schemes help but are mostly limited to 2D composite reflectivity, collapsing the atmosphere into one layer and discarding vertical structure critical for height-dependent dynamics. We introduce Nowcast3D, a gray-box, fully 3D framework that works directly on volumetric radar reflectivity. The end-to-end model couples physically constrained neural operators (advection, local diffusion, and microphysics) with a conditional diffusion model to generate ensemble forecasts with quantified uncertainty. Trained on provincial-scale 3D volumes over a $10.24^\circ \times 10.24^\circ$ region and fine-tuned on a $2.56^\circ \times 2.56^\circ$ city region ($0.01^\circ \approx 1$ km), Nowcast3D provides near-real-time forecasts up to 3 h and outperforms competitive baselines in cross-region and temporal out-of-sample tests. It can also infer wind fields without labeled supervision, supporting physically plausible transport. In a nationwide blind evaluation by 160 meteorologists, Nowcast3D ranked first and was preferred in 57% of post-hoc assessments, surpassing the leading baseline (27%). These results highlight its reliability and operational value for extreme precipitation nowcasting.

翻译：极端降水的可靠临近预报仍然十分困难，因为对流系统在三维空间中具有强烈的非线性、多尺度与非平稳特性。雷达是临近预报的支柱，但现有方法在预测极端降水时面临挑战：基于物理的外推法无法捕捉对流系统的生消演变；确定性学习方法往往过度平滑并低估峰值强度；而纯生成式模型则常常缺乏物理一致性。混合方案虽有益处，但大多局限于二维组合反射率，将大气压缩为单一层面，丢弃了对高度依赖动力学至关重要的垂直结构。本文提出Nowcast3D，一个直接处理体扫雷达反射率数据的灰盒式全三维框架。该端到端模型将物理约束的神经算子（平流、局部扩散与微物理过程）与条件扩散模型相结合，以生成具有量化不确定性的集合预报。通过在$10.24^\circ \times 10.24^\circ$区域（省级尺度）的三维体数据上进行训练，并在$2.56^\circ \times 2.56^\circ$城市区域（$0.01^\circ \approx 1$公里）进行微调，Nowcast3D能够提供最长3小时的近实时预报，并在跨区域与时间外样本测试中优于现有基准方法。该模型还可在无标签监督的情况下推断风场，支持物理上合理的平流输送。在一项由160位气象专家参与的全国性盲评中，Nowcast3D位列第一，并在57%的事后评估中获选为最优方法，显著领先于主要基准模型（27%）。这些结果凸显了其在极端降水临近预报中的可靠性与业务应用价值。