The use of deep learning has increased extensively in the last decade, thanks in part to advances in computing technology (e.g., data storage, graphics processing units) and the digitization of data. In medical imaging, deep learning algorithms have shown promising potential for clinical use due to their capability of extracting and learning meaningful patterns from imaging data and their high performance on clinically-relevant tasks. These include image-based disease diagnosis^1,2 and detection,³ as well as medical image reconstruction,^4,5 segmentation,⁶ and image-based treatment planning.^{7 – 9}

High‑resolution PM _2.5 forecasts are increasingly produced with machine‑learning models, yet practical guidance on how often these models should be retrained and validated remains limited. This study quantifies the impact of retraining frequency and bias correction on air‑quality prediction skill across multiple cities with contrasting emission sources and meteorological regimes, using The Goddard Earth Observing System composition forecast (GEOS-CF) fused with in‑situ observations. Site‑specific models are trained on at least two years of hourly data, with bias correction and alternative update schedules (6–18‑month baselines and 6–12‑month retraining cycles) evaluated using RMSE, R ² , and SHAP‑based feature importance. Bias‑corrected Models consistently improves GEOS‑CF forecasts by more than 107% in R ² and reduces RMSE by over 75%, with annual retraining providing the largest gains (12.41% increase in R ² , 9.88% reduction in RMSE) relative to more frequent updates. SHAP analysis shows that the dominant predictors—and their relative importance—vary by city, with combinations of boundary‑layer height, aerosol optical depth, humidity, wind, and nitrogen oxides driving PM _2.5 levels, demonstrating that a single global pre‑trained model is inadequate and that locally tuned models are required. Together, these results define minimum data requirements, preferred retraining intervals, and the need for site‑specific bias‑corrected models, offering concrete design rules for operational PM _2.5 forecasting systems.