Tropical cyclones (TCs) are some of the most destructive weather events that affect low/mid-latitude regions across the globe. Understanding TC risks in both the present and future climate provides valuable information for stakeholders to inform local policy and preparedness measures. This includes areas that frequently experience and are currently vulnerable to TC impacts as well as regions that infrequently observe TCs and/or may see an increase in TC activity in the future. Two popular tools currently available for studying TC climatology are high-resolution climate models (which directly simulate TCs that can be tracked in model output) and statistical-dynamical downscaling (SDD) models (which utilize a model’s large-scale climatology to generate synthetic storms).

One obvious question is how these techniques differ. If high-resolution models contain TC climatology biases, can we determine if these factors are due to insufficient resolution, parameterization biases, or errors in large-scale steering (among others)? Are TCs generated by SDD methods providing similar answers to those gleaned from Lagrangian storm tracking directly in model data? To assess this, we analyze simulation data from the High-Resolution Model Intercomparison Project (HighResMIP) models. We take TCs tracked by TempestExtremes and compare them to observed landfalls using both IBTrACS and storm tracks objectively defined in reanalyses. We also leverage the SDD TC model described in Lin et al. (2023) to generate a parallel set of tracks derived from daily and monthly HighResMIP model fields. We discuss how this strategy can allow us to both diagnose sources of HighResMIP model biases when representing TCs and assess the validity and utility of SDD approaches for climate applications. Areas of agreement and disagreement will be discussed as well as potential strengths and weaknesses of each approach using the HighResMIP ensemble.