Earth System Models (ESM) are powerful tools for studying the earth system and play an indispensable role in conducting scientific research on disaster prevention and mitigation,climate change,and environmental governance.Traditional weather and climate models rapidly evolve towards ESM,including ocean,sea ice,biogeochemical,and atmospheric chemical processes.At the same time,an increasing number of applications are adopting ESM for weather,climate,and ecological prediction.The current international mainstream trend in developing numerical models is to achieve seamless simulation and prediction by constructing integrated models,simultaneously meeting the needs of weather-climate forecasts and predictions at varying temporal and spatial scales.With improved model complexity and resolution,traditional numerical weather models have rapidly progressed in climate change research and climate prediction.However,challenges remain regarding data assimilation,ensemble coupling,high-performance computing,and uncertainty analysis and evaluation.The combination of artificial intelligence (AI) and meteorology has recently attracted tremendous attention.Based on various deep learning architectures,deep learning models can be trained using powerful computing resources and massive data for weather forecasts in a new scientific paradigm independent of traditional numerical weather models.Some technology companies,such as Huawei,NVIDIA,DeepMind,Google,Microsoft,etc.,as well as domestic and international universities such as Tsinghua University,Fudan University,the University of Michigan,Rice University,etc.,have released several Large Weather Models (LWMs) covering from nowcasting,short-term forecast to medium-term forecast,and even extended-period forecast.For instance,FourCastNet,GraphCast,NowcastNet,Pangu Weather,Fengwu,Fuxi,etc.,show significant advantages and great potential in improving forecast accuracy and accelerating the forecast inference process.For accuracy,except in areas like extreme weather,LWMs have matched or even surpassed that of traditional numerical models.Moreover,with continuous development of deep learning methods,their forecasting precision is steadily increasing.For timeliness,LWMs,leveraging deep neural networks' powerful generalization capabilities,far exceed traditional numerical models' predictive abilities under the same resolution conditions.For computational speed,LWMs have significantly increased inference computation speed compared to traditional numerical models,gradually reduced the enormous computation times required by traditional numerical models.The emergence of LWMs signifies that the cross-fertilization between AI and meteorological fields has reached a new horizon.Although these LWMs have made significant breakthroughs at this stage,their development still faces many challenges,such as the interpretability problem,the generalization and migration challenge,and the over-smoothing problem.The advancement of numerical weather prediction is closely tied to developments in computational and data storage technology,as well as observational techniques.Its application requires interdisciplinary integration,combining insights from various scientific fields.A critical scientific challenge in this field is to foster a more profound integration of numerical weather prediction with emerging information technologies such as artificial intelligence,quantum computing,and digital twins.This challenge also involves tailoring complex and refined component models to meet diverse disciplinary demands and societal needs.Advancing numerical weather prediction within the broader context of earth system science requires a concerted effort to promote cross-disciplinary collaboration,addressing vital scientific questions at the intersection of multiple fields.