The arXiv preprint (2605.24009, May 2026) introduces a diffusion-based post-processor that ingests GFS CAPE fields and five aerosol optical depth species to generate 6-hour afternoon ensemble forecasts over the contiguous United States. The model outperforms both deterministic GFS and full GEFS ensembles on RMSE, CRPS, and Brier scores while allowing explicit control of spread via classifier-free guidance. Because the work remains a preprint, it has not yet undergone peer review; the authors acknowledge that training draws on reforecast archives whose initialization and physics packages changed over time, addressed via a two-stage pipeline that first learns from a larger GFS-only corpus before fine-tuning on the smaller GEFS set. What the paper underplays is the physical mechanism: black-carbon, organic-carbon, and sulfate aerosols rank highest in permutation importance precisely because they alter boundary-layer stability and updraft buoyancy on the same 6-hour timescale the model targets. This finding aligns with earlier observational work (e.g., Rosenfeld et al., Science 2008 on aerosol invigoration) and recent high-resolution LES studies (Morrison et al., JAS 2022) showing that anthropogenic aerosol perturbations can shift CAPE by 300–800 J kg⁻¹ in the central U.S. during summer. The diffusion approach therefore supplies a data-driven route to fold microphysical uncertainty directly into operational ensembles, something current stochastic physics schemes still parameterize rather than learn. For climate adaptation, the framework offers a pathway to recalibrate risk models under changing aerosol emissions—an angle current NOAA and ECMWF ensemble suites have not yet operationalized. Limitations remain: the training domain is U.S.-only, verification is limited to 2020–2024 summers, and extreme CAPE tails (>4000 J kg⁻¹) are underrepresented, raising questions about generalization to future climates with altered aerosol loading.