With rapid urbanization, predicting urban microclimates has become critical, as it affects building energy demand and public health risks. However, existing generative and homogeneous graph approaches fall short in capturing physical consistency, spatial dependencies, and temporal variability. To address this, we introduce UrbanGraph, a framework founded on a novel structure-based inductive bias. Unlike implicit graph learning, UrbanGraph transforms physical first principles into a dynamic causal topology, explicitly encoding time-varying causalities (e.g., shading and convection) directly into the graph structure to ensure physical consistency and data efficiency. Results show that UrbanGraph achieves state-of-the-art performance across all baselines. Specifically, the use of explicit causal pruning significantly reduces the model's floating-point operations (FLOPs) by 73.8% and increases training speed by 21% compared to implicit graphs. Our contribution includes the first high-resolution benchmark for spatio-temporal microclimate modeling, and a generalizable explicit topological encoding paradigm applicable to urban spatio-temporal dynamics governed by known physical equations.

翻译：随着城市化进程的加速，城市微气候预测变得至关重要，因其直接影响建筑能耗需求与公共健康风险。然而，现有的生成式方法与同质图方法在捕捉物理一致性、空间依赖性与时间变异性方面存在不足。为此，我们提出了UrbanGraph，一个基于新颖结构归纳偏置的框架。与隐式图学习不同，UrbanGraph将物理第一性原理转化为动态因果拓扑结构，显式地将时变因果关系（如遮阳效应与对流过程）直接编码至图结构中，从而确保物理一致性与数据效率。实验结果表明，UrbanGraph在所有基线模型上均达到了最先进的性能。具体而言，与隐式图相比，显式因果剪枝技术的应用使模型浮点运算量显著降低了73.8%，训练速度提升了21%。本研究的贡献包括：首个用于时空微气候建模的高分辨率基准数据集，以及一种可推广的显式拓扑编码范式，该范式适用于受已知物理方程约束的城市时空动态建模。