China’s Mazu AI Aids Disaster Prevention in Africa
China’s AI-powered meteorological early warning solution, known as Mazu, is delivering tangible results in disaster prevention across multiple African regions, according to a comprehensive report published by Xinhua. The system, named after the revered Chinese sea goddess, is helping vulnerable communities anticipate extreme weather events with unprecedented precision.
Africa is among the regions most severely affected by climate change, yet it possesses some of the weakest climate adaptation capacities. Extreme weather events frequently disrupt economic activity and threaten lives across the continent. In Djibouti, port workers have long contended with sudden heat waves, strong winds, and intense rainfall that halt operations and damage equipment. In Ethiopia, seasonal flooding and landslides have caused casualties and displacement. Across sub-Saharan Africa, rainfall deficits continue to trigger crop failures and livestock losses.
A Technological Response to a Global Challenge
The Mazu solution, officially released by the China Meteorological Administration (CMA) in July 2025 at the World Artificial Intelligence Conference in Shanghai, represents the world’s first national-level action plan responding to the UN’s Early Warnings for All initiative. The acronym MAZU stands for Multi-hazard, Alert, Zero-gap, and Universal, reflecting its alignment with the initiative’s four pillars of risk assessment, monitoring and warning, information dissemination, and emergency response.
The system integrates data from China’s Fengyun meteorological satellites with the Fenglei, Fengqing, and Fengshun series of AI weather forecasting models, alongside a cloud-based early warning business platform. This architecture allows for customized applications tailored to ports, airports, agriculture, and urban environments.
As of August 2026, the Mazu solution has been deployed in seven countries—Pakistan, Ethiopia, Solomon Islands, Jordan, Sri Lanka, Mongolia, and Djibouti—with more than 40 countries using its cloud-based early warning products. Pan Jinjun, chief engineer of the CMA, described the initiative as developing into “a shared, customizable, and sustainable international public product for developing countries.”
Djibouti: From 1.0 to 2.0
Djibouti, strategically positioned at the gateway between the Red Sea and the Indian Ocean, has been a primary beneficiary. In July 2025, China donated MAZU-Urban 1.0 to the country, providing a first-generation early warning capability. The upgraded MAZU Djibouti 2.0 was formally delivered in July 2026 at the meteorological sub-forum of the 2026 World Artificial Intelligence Conference, as reported by the State Council Information Office.
The upgraded version improves forecast resolution from 9 kilometers to 3 kilometers, provides forecasts up to three days in advance, and updates every six hours. It combines intelligent terminals equipped with meteorological chips and forecasting models into an integrated solution for weather monitoring, forecasting, and warning.
Mohamed Ismail Nour, director of the Djibouti National Meteorological Agency, noted that the 1.0 version proved highly practical, with Chinese teams training multiple local staff members who now work closely with their Chinese counterparts. The system has already improved conditions for port workers, enabling extreme weather warnings up to 24 hours in advance that reach port managers and frontline crews directly.
Ethiopia: First in Africa, Now a Regional Hub
Ethiopia became the first African country to receive the Mazu system, and its experience illustrates the broader capacity-building dimension of the initiative. The Ethiopian Meteorological Institute (EMI) has completed configuration and installation of the system, with local experts now capable of operating it independently after receiving comprehensive training from visiting Chinese scientists.
Asaminew Teshome, deputy director general of the EMI, told Xinhua that Mazu integrates data from different satellite systems with local weather station information to generate more targeted and refined weather and climate data. “We have already begun generating nowcasting and short-term forecasts through Mazu,” he said.
Teshome emphasized that receiving the MAZU platform makes the EMI a pioneer in Africa, with ambitions to serve as a regional knowledge hub by sharing hands-on experience and technical capabilities with other African meteorological service centers. This vision aligns with Ethiopia’s broader engagement in AI cooperation, including its July 2026 signing as a founding member of the World Artificial Intelligence Cooperation Organization.
Cloud Accessibility and Customization
One of Mazu’s defining features is its emphasis on scenario-based application, described by CMA Director Chen Zhenlin as a “one country, one policy” approach. “Island nations need port services, inland countries care about sandstorms—Mazu can develop targeted application scenarios based on needs,” Chen explained.
This flexibility is particularly valuable for African nations with limited meteorological infrastructure. Simplice Chinda Tazo, director of the Cameroon National Meteorological Agency, noted that early warning is one of the most cost-effective ways to reduce disaster losses. “Many African and South Asian countries have sparse meteorological observation networks and limited computing capacity,” he said. “Mazu can be used via the cloud, effectively lowering the barrier to entry, and the solution can be customized to local conditions—this is crucial because different countries face different disaster characteristics.”
The April 2026 release of the new-generation Mazu early warning cloud public version addressed connectivity challenges by adding overseas cloud service nodes, improving platform loading speed by six times and significantly reducing data response time for users in Africa and South Asia.
International Recognition and Expansion
The Mazu solution has drawn high-level international recognition. UN Secretary-General António Guterres praised the initiative at the 2026 World Artificial Intelligence Conference, stating that China’s platforms, satellites, and AI models have helped dozens of countries improve early warning capabilities. “This is exactly the cooperation model the world needs—helping countries protect lives through technology transfer, joint research, and local capacity building,” Guterres said.
Hussen Seid Endris, head of the Climate Diagnostic and Prediction Unit at the IGAD Climate Prediction and Applications Center, told Xinhua that MAZU represents an important and timely opportunity for Africa to strengthen early-warning systems amid increasing exposure to weather and climate-related hazards. He emphasized the value of technical cooperation and knowledge exchange with the CMA, noting that solutions must be tailored to the specific needs and institutional contexts of African countries.
The World Meteorological Organization has given high praise to the Mazu solution and expects it to be further promoted globally, particularly in developing countries.
A Symbol of Joint Response
At the 2026 World Artificial Intelligence Conference, President Xi Jinping announced that China would promote the deployment of the Mazu meteorological intelligent early warning solution in 30 countries. The CMA has also launched the Fenghe meteorological large model global open-source plan and initiated a China-Thailand joint laboratory for meteorological disaster intelligent prediction and early warning.
Since 2023, the CMA has built an early warning capability training system, and since 2024, nearly 1,000 people from over 100 developing countries and regions have participated in China’s meteorological early warning technology training. These efforts reflect what Zeng Qin, director of international cooperation at the CMA, described as a partnership network to jointly tackle extreme weather: “Wherever Mazu is seen around the world, it serves as a symbol of our joint response to extreme weather.”
As climate risks intensify globally, the Mazu solution’s expansion across Africa and beyond represents a significant example of technology transfer and international cooperation in disaster risk reduction. The coming months will show whether this AI-powered approach can continue to scale and deliver on its promise of protecting lives and property in the world’s most climate-vulnerable regions.