可再生能源数据与城市热岛效应的超分辨率建模（Sup3rUHI）引入机器学习方法，将高分辨率城市热岛（UHI）效应嵌入低分辨率的历史再分析数据及未来气候模型数据中。该数据集包含针对洛杉矶和西雅图训练的UHI估算模型，并提供开源软件及美国本土连续48州人口最多的50个城市的额外训练数据。研究展示了这些方法在高分辨率城市微气候建模中评估气候变化影响与降温策略的应用。该数据集旨在为城市规划者提供一种计算高效且可灵活适配的解决方案，以应对各类高温规划问题并优先部署降温策略。开源模型、软件与数据将助力在气候变化背景下构建更具高温韧性与可持续性的城市环境。

Super-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI) introduces machine learning methods to incorporate high-resolution Urban Heat Island (UHI) effects into low-resolution historical reanalysis and future climate model datasets. The dataset includes models trained to estimate UHI in Los Angeles and Seattle, along with open-source software and additional training data for the 50 most populous cities in the contiguous United States. The study demonstrates the application of these methods in evaluating climate change impacts and heat mitigation strategies within high-resolution urban microclimate modeling. The dataset aims to provide a computationally efficient and adaptable solution for urban planners to address various heat planning questions and prioritize heat mitigation strategies. The open-source models, software, and data will contribute to the development of more heat-resilient and sustainable urban environments in the face of climate change.