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China Unveils Earth System Forecasting Strategy for 2025–2035

Sun Ye

/VCG
/VCG

/VCG

The China Meteorological Administration (CMA) announced its Earth System Forecasting Development Strategy (2025–2035) on Thursday, aiming to build a world-class, self-reliant forecasting system by 2035.

China Meteorological Administration Deputy Director Bi Baogui. /CMA
China Meteorological Administration Deputy Director Bi Baogui. /CMA

China Meteorological Administration Deputy Director Bi Baogui. /CMA

CMA Deputy Director Bi Baogui said at a press briefing in Beijing that the initiative addresses both national disaster prevention and climate change adaptation needs.

The strategy adopts a "dual-wheel drive, digital-intelligence integration" approach, according to Bi, combining physics-based numerical forecasting with artificial intelligence to enhance prediction accuracy and efficiency.

There are eight key priorities, including developing multi-sphere-coupled Earth system models, AI forecasting models, and digital infrastructure capable of delivering seamless predictions from minutes to decades.

The strategy unfolds in two phases. The first is to achieve operational next-generation models and unified AI frameworks by 2030, and the second is to complete an integrated Earth system forecasting system with global kilometer-scale and regional hundred-meter-scale capabilities by 2035.

AI models like CMA's "Fengqing" and Huawei's Pangu have demonstrated breakthrough efficiency, reducing forecast computation from hours to seconds or minutes. These advances will support disaster early warning, agricultural planning, renewable energy, and carbon neutrality goals, said Liang Feng, Director of CMA's Forecasting Division.

Liu Zuoting, director of the Xiong'an Meteorological AI Innovation Institute, stressed the institute's role as a "testing ground" for cross-disciplinary innovation in AI forecasting model development.

The strategy also emphasizes international cooperation, particularly with Belt and Road partner countries, to address global climate challenges.

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