LAUSR

Human‐induced climate change is reshaping wind patterns across Canada, posing significant challenges for sectors such as wind energy and infrastructure planning. This study assesses the capability of regional climate models (RCMs) in simulating near‐surface wind speed (WS) across Canada by analysing outputs from various RCM ensembles, which downscale CMIP5 global climate model (GCM) output, including the NA‐CORDEX multi‐model ensemble (at 0.22° resolution) and the CanRCM4 single‐model large ensemble (at 0.44° resolution). These RCM outputs are compared against observational data, two reanalysis data sets (ERA5 and AgERA5), and GCM ensembles from CMIP5 and CMIP6. The evaluation examines the models' ability to replicate historical WS distributions, biases in mean and extreme WS, trends and temporal variability. The findings reveal that, despite the higher spatial resolution of RCMs, their added value over the GCM ensembles is limited, raising concerns about the reliability of RCM‐derived WS projections for climate services without further bias adjustment or statistical downscaling. The inability of both RCMs and GCMs to accurately simulate WS trends diminishes confidence in future WS projections, potentially leading to inadequate risk assessments and insufficient preparation for the impacts of climate change on vital sectors like energy and infrastructure.