Abstract: 0—30 d seamless fine gridded weather forecasts have been initially established to cover fundamental forecast elements in China. In recent years, the advances and applications of deep learning have brought unprecedented changes to different fields. The capabilities of nonlinear mapping, massive information extraction, spatial-temporal modeling and other advantages of deep learning provide new concepts and methods for further improvement of forecast accuracy and refinement. The growing studies on deep learning techniques have been applied widely to weather forecasting, including statistical postprocessing, ensemble forecasting, analog ensemble, statistical downscaling, data-driven forecasting models and extreme weather forecasting. The deep learning techniques have demonstrated a great application potential. However, the application of deep learning in gridded weather forecasting is still at the initial stage. The challenges include algorithm selection, benchmark dataset, multi-source data blending, interpretability, reliability, availability and operational implementation, etc., when introducing it into current Intelligent Grid Forecast System. Review of the progress and challenges of the deep learning at fine gridded weather forecasting in recent years will be helpful for us to better understand deep learning techniques and their application in weather forecasting.