China’s Global Meteorological Early Warning Support Platform: Development and Applications

In response to the critical global challenge of bridging the early warning capability gap—particularly for developing and least developed countries—this paper presents the Cloud-based Global Meteorological Early Warning Support Platform (C-EWS) developed by China Meteorological Administration (CMA). C-EWS embodies a novel operational paradigm designed to overcome the persistent scientific and logistical barriers that hinder effective early warning operations in resource-constrained settings.

C-EWS integrates a lightweight, cloud-based architecture to ensure universal accessibility. Through server-side rendering, asynchronous data streaming, and a unified API gateway, the platform shifts computational burden to the cloud, enabling smooth interactive exploration of multi-gigabyte datasets even over low-bandwidth connections. The platform consolidates multi-source observational data (including Fengyun satellite and WMO GTS observations), global NWP models (CMA-GFS, ECMWF-IFS, NCEP-GFS, ICON, JMA-GSM), and AI forecasts ('Fengqing', ECMWF-AIFS) into a unified, interactive web environment.

Unlike existing international platforms that offer either single-model expert products or basic multi-model visualization, C-EWS uniquely enables comprehensive multi-model diagnostic analysis, integrating both physics-based and AI-driven forecasts for enhanced uncertainty assessment. The platform embeds over 20 interactive analytical tools for multi-model comparison, forecast stability evaluation, and vertical profiling, enabling forecasters to rapidly analyze three-dimensional circulation patterns and assess model biases.

C-EWS delivers a suite of multi-model-based objective early warning products covering multiple hazard types—including tropical cyclones, sand and dust storms, extreme heat, heavy precipitation, gale winds, and floods. Verification against 2025 forecast products demonstrates substantial skill improvements: for heavy rainfall warnings, TS reached 0.226–0.158 for short-range forecasts—improvements of over 26% compared to NWP model forecasts. Operational efficiency has also been significantly enhanced: the time required to produce comprehensive Global Hazardous Weather Bulletins has been reduced from approximately 3.5 hours to 1 hour (a 71% improvement), while the volume of early warning products issued has increased by 136%. The platform also employ a co-development framework, enabling partner countries to co-create tailored solutions.

Since its operational deployment, C-EWS has supported forecasting for over 50 severe weather events across more than 30 countries and regions. Feedback from stakeholders confirms its practical value. The platform also supports multilateral mechanisms including WMO's Multi-Model Integrated Forecasting and Application (MMIFA) pilot project, the WMO Coordination Mechanism's (WCM) Hydrometeorological Weekly Scan, and the South Asia Hydromet Forum (SAHF) forecast consultations.

This work establishes a scalable and scientifically robust pathway to broaden access to advanced forecasting capabilities for developing nations, directly contributing to the United Nations' "Early Warnings for All" (EW4ALL) initiative and strengthening global climate resilience. Future developments will focus on expanding data source integration, operationalizing hybrid NWP+AI approaches for sub-seasonal to seasonal forecasting, and developing self-service configuration tools for more scalable customization.