Regional Climate Models (RCMs) are the primary climate information available to public stakeholders and city-planners to support local adaptation policies. However, with resolution in the order of ten kilometres, RCMs do not explicitly represent cities and their influence on local climate (e.g. Urban Heat Island; UHI). Downscaling methods are required to bridge the gap between RCMs and city scale. A statistical–dynamical downscaling methodology is developed to quantify the UHI of the city of Paris (France), based on a Local Weather Types (LWTs) classification combined with short-term high-resolution (1-km) urban climate simulations. The daily near-surface temperature amplitude, specific humidity, precipitation, wind speed and direction simulated by the RCMs are used for the LWTs attribution. The LWTs time series is associated to randomly selected days simulated with the mesoscale atmospheric model Meso-NH coupled to the urban canopy model Town Energy Balance to calculate the UHI corresponding to the successive LWTs. The downscaling methodology is applied to the EURO-CORDEX ensemble driven by the ERA-Interim reanalysis, and evaluated for the 2000–2008 period against station observations and a 2.5-km reanalysis. The short-term dynamical simulations slightly underestimate and overestimate near-surface minimum and maximum air temperature respectively, but capture the UHI intensity with biases in the order of a tenth of a degree. RCMs show significant differences in the variables used for the LWTs attribution, but the seasonal LWT frequencies are captured. Consequently, the reconstructed temperature fields maintain the small biases of the Meso-NH simulations and the statistical–dynamical downscaling greatly improves the UHI compared to the raw data of RCMs.

区域气候模式（RCM）是公共利益相关者和城市规划者可用来支持地方适应政策的主要气候信息。但是，RCM分辨率大约为10公里，因此不能明确表示城市及其对当地气候的影响（例如Urban Heat Island； UHI）。需要缩小规模的方法来缩小RCM与城市规模之间的差距。基于局部天气类型（LWTs）分类和短期高分辨率（1 km）城市气候模拟，开发了一种统计动态降尺度方法来量化巴黎市（法国）的UHI。由RCM模拟的每日近地表温度振幅，比湿度，降水，风速和风向被用于LWT的归属。LWT的时间序列与中尺度大气模型Meso-NH模拟的随机选择天数相关联，该模型与城市林冠模型Town Energy Balance耦合以计算与连续LWT对应的UHI。降尺度方法应用于由ERA-Interim重新分析驱动的EURO-CORDEX集合，并针对2000-2008年期间的台站观测和2.5 km重新分析进行了评估。短期动力学模拟分别略微低估和高估了近地表最低和最高气温，但捕获的UHI强度的偏差约为十分之一度。RCM在用于LWT归因的变量中显示出显着差异，但捕获了季节性LWT频率。所以，