Automatic mapping of urban heat islands based on high-resolution climate simulations with application to French cities

The identification of rural reference areas often requires predefined information on regional environmental characteristics or established climatic classifications, which may add subjectivity to studies and cannot be reproduced in new areas. This study presents a novel methodology that uses kilometric-scale climate simulations to systematically identify suitable rural reference areas for cities and estimate the associated urban heat island (UHI) intensity at the grid cell level. The method in question, called MOD_Mask, integrates the classification of ECOCLIMAP land cover, derived from high-resolution CORINE Land Cover data, to extract urban areas without prior regional knowledge. The method was tested across metropolitan France, including cities of various sizes, from the densely populated Paris region to the medium-sized city of Dijon. Data from local weather stations in these two cities was used to evaluate the automated UHI mapping method. MOD_Mask successfully identified and mapped areas most exposed to UHI effects with high precision, helping to investigate population exposure and its effects within 12 French cities. This methodology has significant potential for future climate projections and scenario analyses, providing valuable insights into important climate indicators. It provides spatial UHI diagnostics to help decision-makers understand UHI conditions and develop climate adaptation strategies. The framework can also be applied to other regions and expanded to include more climate variables.