Cumulus convection parameterization (CCP) is a core component of numerical weather prediction (NWP) models, which includes triggering conditions, cloud models, closure schemes, and feedback mechanisms, along with their implicit assumptions. As NWP models increasingly operate at convection-permitting resolutions, improving the scale awareness of CCPs is essential for enhancing model performance across varying resolutions. This study aims to systematically review recent progress in scale-aware CCP and to clarify the underlying physical assumptions and methodological developments. Based on a comprehensive analysis of the iterative evolution of four commonly used convection parameterization schemes (i.e., Arakawa–Schubert, Kain–Fritsch, Grell, and Tiedtke) in operational models, three key advancements in scale-aware CCP are identified. First, the traditional assumption of a negligible convective area fraction has been relaxed, allowing a non-negligible convective area fraction. This is achieved by introducing an explicit updraft area fraction parameter linked to grid resolution, which establishes its quadratic relationship with vertical eddy transport and facilitates a unified parameterization framework. Second, the representation of multiscale atmospheric characteristics and interactions has been enhanced by developing resolution-dependent formulations for critical parameters. These parameters (e.g., convective adjustment timescales, entrainment and detrainment rates, convective available potential energy, convective inhibition, and grid-scale vertical velocity) are systematically integrated into triggering conditions, closure schemes, and cloud microphysical models. Third, the “no-net-mass transport” assumption is addressed to achieve improved physics–dynamics coupling. To compensate for convection-induced mass perturbations, convection parameterization schemes now directly incorporate convective mass sources/sinks into the continuity equation or spread subsidence to neighboring grid points. Finally, we reflect on fundamental challenges in understanding the multiscale interaction nature of scale-aware CCP and its associated requirements for refined cloud microphysical understanding, and outline future development directions.

Numerical weather prediction (NWP) is the core technology for weather forecast and disaster prevention and mitigation. The research and operational applications of NWP have always been highly valued in China, and have achieved great progress with an appreciable international influence in the theories, algorithms, and operational system developments. This paper first summarizes the scientific and technological evolution of NWP in China, and then focuses on the current status and recent updates of the two homemade global NWP systems: GRAPES (Global/Regional Assimilation and PrEdiction System) and YHGSM (YinHe Global Spectral Model). (1) GRAPES possesses both deterministic and ensemble forecast systems, with global (regional) model versions running on 12–50-km (3–10-km) resolutions. Significant improvements have been made on its dynamic core, four-dimensional variational (4D-Var) assimilation, satellite and radar data assimilation, ensemble forecast, and cloud microphysics schemes, and so on. It is capable to perform subseasonal to seasonal forecast and has incorporated an atmospheric chemistry model, typhoon numerical forecast model, and ocean wave model. (2) YHGSM continues to follow the development route of spectral models, featured prominently with a dry-mass conserved spectral dynamical core, ensemble 4D-Var assimilation, coupled ocean–land–atmosphere ensemble forecast, and the medium-term and monthly-extended global high-resolution forecast as the baseline. These NWP systems autonomouly developed by the China Meteorological Administration and the national defense insitution benefit from long-term adherence to the national science and technology development strategies and close research to operation practices.