Can a Chinese AI weather system help Pakistan manage the impacts of a changing climate?

The first time Muhammad Irfan Virk entered the China Meteorological Administration (CMA) in Beijing, in 2024, he was captivated. “I was proud of how far we had leapfrogged technologically, especially over the last two years,” said the director of Pakistan’s National Weather Forecasting Centre. “But the CMA’s forecasting office was something else – it was almost sci-fi.”

Virk was part of a team of Pakistan Meteorological Department forecasters sent to the CMA to help tailor its weather forecasting platform for use in Pakistan.

The system is known as MAZU, short for “multi-hazard, alert, zero-gap and universal”. It is also the name of a traditional Chinese sea goddess known as the protector of sailors.

The AI system cannot stop extreme weather, said Zaheer Ahmed Babar, the Pakistan Meteorological Department (PMD)’s chief meteorologist and acting director general. But by providing earlier warnings, it can “help forecasters make better and wiser decisions”, he said. “How we use that information to reduce losses is up to us.” The PMD has been using a version of MAZU tailored for Pakistan since October 2025, said Yin Shengxin, science attaché of the Chinese Embassy in Islamabad. This is alongside other early warning tools, including data from other weather model datasets and satellites.

“Given Pakistan’s pressing needs for early warning capabilities and disaster risk reduction, the two sides jointly developed and deployed the MAZU system, making Pakistan the first country to benefit from this innovative solution,” said Yin.

The CMA and PMD have a long history of close collaboration, he told Dialogue Earth. Yin added that the cooperation between China and Pakistan on MAZU is being carried out “under the South-South cooperation on climate change” by the Chinese government.

In the aftermath of the 26 August floods in regional neighbour Nepal and China, MAZU is also being used to assess and forecast local weather conditions for organising rescue efforts, reports China Daily. Using the system, Chinese authorities have reportedly been providing Nepal with daily updates in aspects including the development and risk forecasts of newly formed barrier lakes, and emergency hydrological monitoring.

But while AI-weather modelling experts Dialogue Earth spoke to broadly welcome the use of the Chinese model, they say it is not a magic bullet for early warning systems. They also say it is too early to tell whether it can be accurate in Pakistan’s local climate and geographical context.

MAZU takes centre stage

At an April press conference announcing further international cooperation on MAZU’s expansion, CMA administrator Chen Zhenlin said early warning systems offer a cost-effective way to protect lives and property as extreme weather threatens food and energy security.

According to Chinese news reports, at the 2026 World Artificial Intelligence Conference held in Shanghai in July, MAZU’s booth proved popular, drawing huge crowds that included diplomats, engineers and journalists.

Its popularity may have been boosted by Chinese President Xi Jinping namechecking the system in his keynote speech at the conference. “To further support global AI development and advance global AI capacity building, I hereby announce that in the next five years, China will provide developing countries with 5,000 opportunities in AI training and seminar programs… and enable 30 countries to use the AI-powered meteorological warning system, or MAZU,” he told attendees.

Babar of PMD said MAZU can be tailored to a country’s geography, infrastructure and disaster risks by integrating local meteorological data. Pakistani researchers and forecasters were trained to use a version calibrated to Pakistan’s data, he noted.

MAZU has also been deployed in Djibouti, Ethiopia, Mongolia, Jordan, Sri Lanka and the Solomon Islands. China is also supporting over 40 countries with cloud-based, virtual trials of the system.

“What makes the platform valuable is that it brings conventional meteorology, satellite and radar data, ground-station observations and local calibration together with AI insights and warnings,” said Asim Javid, CEO of AI Geo Navigators, which uses AI, mapping data and remote sensing to gauge weather and environmental risk. “Having all these inputs integrated in one place changes how forecasters work.”

From insight to real-world validation

While AI-based weather tools support early warning systems, these are not “stand-alone systems sufficient to address our gaps… At least, not yet,” said climatologist Imran Khalid.

An effective early warning system, Babar explained, requires a whole-of-government approach built around four basic components: disaster risk management, detection and forecasting, dissemination and communication, and preparedness and response.

This holistic approach is endorsed by Junaid Yamin, co-founder of WeatherWalay, a Pakistani subscription weather forecast service. But the success of the hydrometeorology sector – the study of water and energy transfer between land and atmosphere – ultimately depends on effective public-private collaboration, he said.

“No single institution can tackle climate change alone. It requires breaking silos, sharing data and aligning government, business and civil society around a common mission,” said Yamin. In countries with strong weather systems, he said, governments, businesses and communities work together, from collecting data and issuing warnings to taking action.

Javid echoed this, arguing that weather data should be “open source”.

“Rain and heat trigger many weather-related hazards, affecting businesses from telecom and banking to agriculture,” he said. “Access to weather data can help businesses design products that reduce disaster losses.” The WMO has been pushing for more “free and unrestricted exchange” of observational weather data through its 2021 unified data policy and its 2024–27 strategy, the latter emphasising partnerships with the private sector.

Only as good as the data feeding it

Explaining how weather forecasts are generated, Babar said the atmosphere is “inherently chaotic”. Even small changes in air pressure, temperature, wind and humidity can make accurate forecasting impossible through conventional methods alone, he noted.

That is where systems like MAZU can help. “It can solve complex mathematical equations in seconds, enabling forecasters to make faster, more timely decisions,” noted Babar.

But these systems are only as good as the weather data feeding it. “Unless we have good data, we will still be guessing and we will still be blind to the [potential] weather impacts,” said Khalid. Such programmes can predict major events such as monsoons and heatwaves. However, precise forecasts – such as when the monsoon will reach a particular valley in a mountainous region, or whether heat will trigger a glacial lake outburst flood – require dense, evenly distributed radar coverage that Pakistan lacks, he noted.

“Pakistan is a hard case,” noted Javid. Ground stations are sparse, and in the country’s northern mountainous regions, terrain elevation can change dramatically over a few kilometres. “Events that kill people – highly localised extreme rainfall and flash floods – happen at scales smaller than most global models [can forecast],” he noted. MAZU cannot predict weather at the 1-km level, he said, and neither can most US and European models, which generally operate at around a 9-km resolution.

Another problem, said Javid, is that it is commonly assumed – even by decision makers – that AI systems automatically produce highly accurate results. “It still depends entirely on the observations and models behind it,” he explained. “A model learns from what has happened before, and the events we most need warnings for are the ones with the thinnest historical record.”

These gaps limit how precisely AI can forecast weather at the local level. “Without detailed ground data, AI tools can effectively hallucinate, producing confident local forecasts with no real observational basis, resulting in serious consequences,” Khalid warned.

He said models can miss highly localised events, such as intense rain over a few neighbourhoods, or a cyclone’s precise landfall timing and location, leaving communities unprepared. “This is why localised radar data is crucial.”

Floods in 2016 badly impacted Peshawar in Pakistan’s north-western Khyber Pakhtunkhwa province. The city, located in a valley, is vulnerable to flash floods from heavy rainfall in the surrounding mountains (Image: IMAGO / Xinhua / Alamy)

Model outputs need to be independently cross-checked, while the systems themselves must be trained to account for Pakistan’s ground realities, he emphasised. This includes training on historical weather patterns and flagging uncertainties about results it produces where there are data gaps. Its outputs must also account for institutional constraints such as delays in communication between government agencies, and lack of sufficient expert staffing and monitoring equipment, Khalid noted.

But the challenge may extend beyond data and equipment. He questioned the PMD’s technical capacity to manage the system, citing a World Bank-funded weather radar project that was ultimately scrapped, and an obsolete existing radar network long overdue for replacement.

Javid pointed to another consideration: accountability. The use of an AI-supported system such as MAZU introduces additional model outputs – or predictions based on processed data – and automated guidance. These should be documented, while the authorised PMD forecaster retains responsibility for putting out the public warning, he noted. “If a warning goes out, or an event is missed, someone has to be able to explain what produced that call. That needs traceability [of technical decisions] and a forecaster in the loop,” he said. “Every warning should have a clear audit trail showing the observations, model outputs, thresholds and professional judgement behind it.”

He added that MAZU should make this judgement “sharper, not optional”.

To see if MAZU really works better than existing systems, Javid said he would use three simple measures, and “set the rules before the season starts, not after seeing what happened”.

First, he said, he would test how accurate MAZU’s forecasts are against PMD’s existing system and an independent benchmark, such as a recognised global forecasting system.

The second test would be hazard detection: “Can MAZU accurately identify dangerous weather events, not just predict ordinary weather?”

The third would be its practical value: how much extra warning time does it provide, and how many false alarms does it generate? “For a deputy commissioner [the most senior administrator of a district], three extra hours to prepare can matter more than a small gain in temperature accuracy.”

He added that he would test it for “two monsoon seasons at minimum, along with winter and mountain [terrain] cases. If MAZU proves better on forecast skill, hazard detection and lead time, it adds real value. Otherwise, it’s a well-designed platform, not a validated one”.