This is a timely paper given the recent rise in data-driven and AI-based weather forecasting. It offers two key contributions. First, the paper provides (potentially the first, but at least one of the first) comparisons of AI-based and physics-based weather forecasting models based on station data (rather than the commonly used comparisons based on gridded ERA5 data). And second, the paper assesses and quantifies the effect of statistical post-processing on forecasts from AI-based weather models, which may also be the first of its kind.