Understanding tropical cyclone (TC) risks in both the present and future climate provides valuable information for stakeholders to inform local policy and preparedness measures. Two popular tools currently available for studying TC climatology are high-resolution climate models (GCMs, which directly simulate TCs that can be tracked in model output) and statistical-dynamical downscaling models (SDD, which utilize a model's large-scale climatology output to generate large numbers of storm trajectories). Historically, these have been considered two disparate tools and are rarely used in concert with one another.

Here, we employ these tools to understand better each approach's strengths and weaknesses in simulating TC climatology. To achieve this, we analyze high-resolution reanalyses and simulation data from High-Resolution Model Intercomparison Project (HighResMIP) models. We take TCs tracked by TempestExtremes and compare them to both IBTrACS and ERA5. We then leverage an open-source SDD TC model to generate a parallel set of tracks derived from the same HighResMIP model fields. We examine several climatological metrics, storm track patterns, genesis and landfall points, and seed conversion rates for each product. We discuss potential paths for improving SDD models with higher-frequency GCM forcing and how SDD models can inform GCM development by offering insights into how large-scale climatology contributes to TC biases.