Sub-seasonal weather forecasting is a notoriously intricate domain due to the complex dynamical interactions occurring within the Earth’s atmosphere during this intermediary timeframe, often spanning two to six weeks. Traditional weather models, predominantly physics-based and reliant on atmospheric equations, struggle with accuracy in this range because subtle nonlinear processes and chaotic atmospheric behavior obfuscate clear signal extraction. The AI Weather Quest challenges researchers worldwide to elevate forecast fidelity by innovatively integrating AI techniques with conventional dynamical outputs, leveraging machine learning to interpret, adjust, and refine predictions dynamically.

The competition has attracted participation from 42 teams representing 15 countries, embodying a diverse and global commitment to improving sub-seasonal weather models. Throughout the contest, teams submit weekly forecasts focused on core meteorological variables: temperature, mean sea-level pressure, and precipitation. These submissions are rigorously scored against real-world weather developments, and results are transparently published and continuously updated on the AI Weather Quest website, fostering a communal environment of real-time feedback and benchmarking that accelerates collective learning and progress.

One notable triumph in the recent competition window covering December 2025 to February 2026 was attained by the MicroEnsemble team, spearheaded by scientists at Microsoft. Their accomplishment is not merely in topping the leaderboard but achieving consistent superior performance across diverse weather variables and lead times. Their methodology involves advanced AI post-processing techniques layered atop ECMWF’s state-of-the-art numerical forecasts, merging the deterministic strengths of traditional models with the data-driven adaptability of AI. This hybrid approach emphasizes probabilistic forecasting, which better captures uncertainties and ultimately supports more informed societal decision-making.

Lester Mackey, Senior Principal Researcher at Microsoft Research and a representative of the MicroEnsemble team, elucidated their success in Bayesian and engineering precision: a multidisciplinary expertise spanning meteorology, statistics, and machine learning allows them to tackle the problem from multiple scientific angles. The team’s insights underscore a fundamental principle: grand challenges in weather prediction require collaborative integration of domain knowledge and algorithmic innovation, rather than reliance on any single paradigm. Their journey illustrates the value of iterative development fuelled by the competition’s transparent benchmarking environment.

The contest’s leaderboard remains fiercely competitive. The Chinese team LP secured a strong second place overall, excelling particularly in forecasting at three-week lead times. Remarkably, LP’s precipitation prediction algorithm is highly efficient—its simplicity and fast execution enable it to run on standard computing hardware without the need for specialized resources such as GPUs. This accessibility highlights an essential aspect of democratizing AI-driven weather forecasting, enabling institutions with limited computational capacity to contribute meaningfully to innovation.

ECMWF’s own research team ranks highly as well, completing the podium and demonstrating leadership in purely data-driven AI models, including their Artificial Intelligence/Integrated Forecasting System (AIFS). Diverging from hybrid techniques reliant on physical model outputs, the AIFS exemplifies pure machine learning approaches using historical observational data alone. ECMWF’s Jakob Schloer emphasizes the dual thrill and strategic advantage of these efforts, stressing how competitive yet collaborative testing environments drive rapid methodological progress and cross-pollination of ideas throughout the weather forecasting community.

A particularly compelling element of the AI Weather Quest is its genuinely global participation and impact. Beyond traditional meteorological powerhouses in Europe, China, and the United States, emerging voices from African nations like Kenya, South Africa, and Morocco as well as South Korea and Peru have entered the fray. These contributions are vital for expanding operational forecasting capabilities across diverse climatic zones, especially where ground-truth data may be sparse and early warning systems critically underdeveloped. The Kenyan Fahamu team’s application of Anemoi technologies epitomizes the potential for AI to strengthen weather resilience and anticipatory disaster response in developing countries.

Nishadh Kalladath, from the IGAD Climate Prediction and Applications Centre (ICPAC), highlights how AI Weather Quest fosters collaboration among operational centers, researchers, and developers. This synergy is pivotal for integrating cutting-edge machine learning methods into existing early warning frameworks, thereby bridging research breakthroughs and practical societal benefits. The emphasis on sub-seasonal forecast reliability supports anticipatory action in vulnerable regions, reinforcing the humanitarian dimension of this scientific enterprise.

Sub-seasonal forecasts themselves occupy a critical niche between broad seasonal outlooks and short-range forecasts. Unlike seasonal predictions, which provide general probability trends over several months, sub-seasonal models yield temporally and spatially refined insights—down to country or regional scales—across 2–6 week horizons. This enhanced granularity is essential for precise emergency planning, such as directing evacuations, mobilizing medical support, or stockpiling necessary supplies ahead of anticipated weather threats, thus directly mitigating risks to life and infrastructure.

The AI-driven approaches showcased in the Weather Quest emphasize probabilistic predictions, capturing the inherent uncertainties of atmospheric conditions rather than over-relying on single deterministic outcomes. This probabilistic framing aligns well with decision-making in real-world scenarios where risk assessments and resource allocations must consider variability and rare but impactful events. Machine learning models excel at quantifying these uncertainties by uncovering subtle patterns in vast meteorological datasets often overlooked by traditional physics-based simulations.

Organized under the auspices of the European Union’s Destination Earth initiative and formally endorsed by the World Meteorological Organization’s Integrated Processing and Prediction System pilot, the AI Weather Quest exemplifies a new paradigm in international scientific cooperation. By providing a transparent, standards-based platform for real-time evaluation of AI methodologies, it catalyzes accelerated innovation and fosters trust through openness—addressing public skepticism often associated with black-box AI applications in critical forecasting domains.

As the contest enters its midpoint, the lessons learned underscore the tremendous potential of hybrid AI-physics systems as well as purely data-driven frameworks. Continued participation and iterative model refinement promise not only incremental forecast accuracy gains but also deeper scientific understanding of atmospheric processes within the sub-seasonal window. This growth trajectory, powered by diverse teams worldwide, primes AI Weather Quest as a watershed moment in the evolution of meteorological science and early warning capability.

Looking ahead, ECMWF plans to keep expanding the contest’s scope, encourage inclusive participation, and further strengthen collaboration networks to push boundaries in sub-seasonal weather prediction. Regular webinars featuring leading contestants facilitate knowledge exchange and community building. Ultimately, the AI Weather Quest is more than a competition—it is a dynamic hub fostering a global collective mission: to harness next-generation AI technologies for safer, more resilient societies coping with the escalating challenges of climate variability and extremes.

Subject of Research:
Sub-seasonal weather forecasting and artificial intelligence integration in meteorological models.

Article Title:
Advancing Sub-Seasonal Weather Prediction: Inside ECMWF’s AI Weather Quest Competition

News Publication Date:
Not specified; content references period from December 2025 to February 2026.

Web References:

• AI Weather Quest website: https://aiweatherquest.ecmwf.int/
• ECMWF blog post: https://www.ecmwf.int/en/about/media-centre/science-blog/2026/ai-weather-quest-2026
• IGAD Climate Prediction and Applications Centre: https://crafd.io/projects/icpac-climatemodeling

Image Credits:
ECMWF’s AI Weather Quest

Keywords:
Sub-seasonal forecasting, artificial intelligence, machine learning, numerical weather prediction, probabilistic forecasts, ECMWF, AI Weather Quest, meteorology, climate resilience, early warning systems, hybrid AI-physical models, global collaboration

Urban areas worldwide are increasingly vulnerable to extreme climatic events such as severe heatwaves, flash floods, dynamic storm systems, and accelerated air pollution. These urban climate threats pose serious risks to human health, infrastructure integrity, and economic stability. Recognizing this urgent need, the Urban-PREDICT project—an acronym for Predictions, Risk Assessments, Early Warnings, Data Integration, Inclusive Governance, Community Awareness, and Transformative Actions—has been launched to develop next-generation urban-scale hazard forecasting capabilities. This ambitious effort aims to leverage state-of-the-art scientific tools to provide cities with timely, precise, and actionable early warning systems tailored to their complex microclimates.

At the helm of this initiative is Professor Fei Chen, Associate Head and Professor of the Division of Environment and Sustainability (ENVR) at HKUST. Chen’s leadership unites an interdisciplinary global consortium of researchers spanning six continents, integrating expertise in atmospheric modeling, artificial intelligence, climate resilience, and urban sustainability. The newly established ICO, situated within HKUST’s Atmospheric Research Center, will orchestrate this international network by coordinating research activities, facilitating demonstration projects in diverse urban contexts, and fostering collaborations between academia, public agencies, and private stakeholders.

HKUST’s role as the host institution places it at the forefront of translating scientific advancement into practical urban climate solutions. Professor Alexis Lau, Head of the ENVR Division and Director of the ICO, underscored the university’s commitment to addressing pressing urban challenges specific to Hong Kong, such as intense torrential rainfall events, urban heat island phenomena, and deteriorating air quality. Lau emphasized the importance of developing resilient urban models that not only anticipate climatic hazards but also empower city planners and communities to enact effective adaptive strategies, thereby protecting vulnerable populations and critical infrastructure.

Central to the Urban-PREDICT framework are four foundational pillars: ultra-high-resolution atmospheric modeling, AI-driven predictive analytics, advanced early-warning communication systems, and comprehensive community preparedness programs. Professor Chen articulated that the project harnesses HKUST’s cutting-edge research strengths in artificial intelligence and climate science to bridge the gap between complex scientific forecasts and actionable societal applications. This integrated approach ensures that early warnings are not only scientifically robust but also accessible and relevant to diverse urban stakeholders.

Dr. Estelle de Coning, Chief of the WWRP at WMO, lauded the collaboration as a transformative advancement in urban climate resilience. She highlighted the ICO at HKUST as a critical nexus for global knowledge exchange and innovation dissemination, enhancing cities’ ability to anticipate weather phenomena and proactively mitigate associated risks. This collaborative ethos reflects WMO’s vision of science-driven societal impact, leveraging world-class research environments to catalyze integrated solutions for local and global climate challenges.

Arthur Lee, Commissioner for Climate Change of the Environment and Ecology Bureau of the Hong Kong SAR Government, reiterated the government’s dedication to advancing climate action through inclusive, multisectoral engagement. Lee stressed that the launch of Urban-PREDICT’s ICO represents a pivotal juncture in building a sustainable urban future, one that demands coordinated efforts spanning governmental bodies, industry sectors, and civil society to foster robust climate resilience mechanisms.

The inauguration coincided with the Urban Climate Prediction and Resilience Roundtable, convened as a capstone event preceding HKUST’s 35th anniversary celebrations. This forum provided a platform for visionary discourse led by Professors Fei Chen and Soledad Ferrari, Co-Chairs of the Urban-PREDICT Project. They elaborated on the scientific roadmap designed to merge detailed urban-scale climatic modeling with policy frameworks, thereby enhancing cities’ capacities to translate early warning signals into effective climate adaptation strategies that reduce vulnerabilities.

Two high-level expert panels, moderated by Professor Alexis Lau and Professor Christine Loh, Chief Development Strategist at HKUST’s Institute for the Environment, engaged diverse representatives from meteorological agencies, government departments, humanitarian organizations, and industry groups. These discussions spotlighted science-based early warning systems for urban hazards, innovative protective measures for at-risk populations, and the indispensable role of insurance and private sector entities in fostering systemic resilience against multi-hazard urban climate risks.

As the ICO begins its operational phase, HKUST and its global collaborators are poised to integrate ultra-high-resolution weather prediction models, advanced machine learning algorithms, and socio-economic insights to deliver unprecedented precision and timeliness in urban hazard forecasts. This holistic scientific paradigm is engineered to not only inform policymakers and emergency responders but also empower communities through inclusive governance, enhancing overall urban resilience and sustainability in the face of mounting climatic uncertainties.

Urban-PREDICT’s commitment to bridging the divide between cutting-edge research and actionable impact marks a transformative approach in urban climate science. By fostering inclusivity and multi-disciplinary innovation, the initiative seeks to demonstrate replicable models for hazard prediction and resilience that can be customized for cities worldwide, particularly those grappling with unique climatic threats and socio-economic vulnerabilities.

Looking ahead, the ICO at HKUST plans to deepen integration of AI technologies with meteorological science to refine predictive accuracy, extend real-time early-warning dissemination through novel communication platforms, and enhance community engagement through educational programs and participatory governance frameworks. These efforts collectively aim to fortify cities’ adaptive capacity, safeguard lives, and reduce long-term economic losses associated with climate-induced urban hazards.

Ultimately, Urban-PREDICT stands as a paradigm shift in urban climate governance, emphasizing collaborative research and innovation that transcends disciplinary boundaries and geographic borders. HKUST’s leadership through the ICO exemplifies the vital role academic institutions play in mobilizing scientific knowledge towards resilient, sustainable urban futures amidst accelerating global climate change.

Subject of Research: Urban climate science, hazard prediction, and resilience building through advanced modeling and AI integration.

Article Title: Not specified.

News Publication Date: Not specified.

Web References: Not specified.

References: Not specified.

Image Credits: HKUST

Keywords: Climate change, Urban climate science, Weather prediction, AI in climate modeling, Early warning systems, Urban resilience, Extreme weather events, Sustainability, Cross-sector collaboration, WMO, World Weather Research Program, Atmospheric research.