The AI Weather Quest, organised by the European Centre for Medium-Range Weather Forecasts (ECMWF), is an ambitious international competition designed to harness artificial intelligence (AI) and machine learning (ML) in advancing sub-seasonal weather forecasting.
Since its launch, the Quest has brought together 250+ participants across 55+ international teams, submitting with 100+ AI/ML models, helping set a new benchmark for sub-seasonal prediction.
See how the latest models are performing on our leaderboards and join the Quest to add your forecasts to the challenge using the AI-WQ-package.
Visualise the forecasts submitted by the participating teams.
To learn more about how the portal works, access the portal’s guide.
Discover the teams and models behind the most skilful forecasts, along with their scores.
For details on how the Leaderboards page works, see the Leaderboards page guide.
The AI Weather Quest brings together participants from around the world to tackle one of meteorology’s most complex challenges: improving the accuracy and reliability of sub-seasonal forecasts beyond traditional limits.
The competition serves as a unique platform to benchmark state-of-the-art AI/ML-based sub-seasonal predictions in an operational-style setting, using real-time forecasts and open data and transparent overviews of model design to support accessibility, reproducibility and fair comparison between approaches.
It is open to individual participants and teams of up to 10 members from international corporations, forecasting institutions, AI/ML experts, meteorologists, researchers, students and other interested communities. No prior experience in weather forecasting is required, only a strong interest in applying AI/ML to real-world challenges and access to the computational resources needed to develop and train models.
The Terms & Conditions define the official framework of the AI Weather Quest. While additional details are provided on this website, they remain the definitive reference for all aspects of the competition.
Sub-seasonal forecasting fills the gap between short and medium-range weather forecasts (up to 15 days) and long-term seasonal outlooks (2 to 6 months), focusing on predicting weather conditions in the critical weeks between. Traditional models, which use dynamical equations to predict the evolution of atmospheric processes, struggle at the sub-seasonal timescale, as their accuracy diminishes over time, and slower-changing environmental factors like sea surface temperatures provide limited insights.
Despite these challenges, sub-seasonal forecasts are essential for sectors like energy, agriculture, and disaster risk reduction. Advancing these predictions can improve resource management, enhance preparedness, and reduce the impact of extreme weather events, transforming decision-making across industries.
AI and ML technologies are revolutionising forecasting by uncovering patterns in vast, complex datasets where traditional methods face limitations. The use of AI/ML methods is increasingly being adopted by leading forecasting centres and tech companies to drive innovation and improve predictions.
Huge, complex and chaotic Earth system
Incredible volumes of data from various datasets
Transformative potential of AI and ML in weather forecasting
The ECMWF AI Weather Quest encourages participants to harness transformative capabilities offered by AI/ML technologies, whether by building new data-driven models trained on historical data or enhancing sub-seasonal predictions by processing existing forecast data. This competition aims to push sub-seasonal forecasting to new levels of accuracy and reliability.
The AI Weather Quest is designed as a long-term benchmarking and learning framework for AI/ML-based sub-seasonal forecasting. Participants are expected to submit real-time, weekly forecasts that cover two lead-time windows: days 19–25 and days 26–32. These forecasts are evaluated, with rankings displayed on the AI Weather Quest leaderboards.
Teams submit global forecasts for weekly-mean near-surface temperature, precipitation, mean sea level pressure, with forecasts for Madden-Julian Oscillation phase probabilities and tropical storm days to be introduced soon. See the Competition Structure for further details.
The AI Weather Quest remains flexible: teams can join at any point in the competition and participate in as many competitive periods as they choose. Throughout the Quest, participants have ongoing opportunities to refine, improve and document their models, contributing to a transparent and collaborative benchmarking environment.
ECMWF, one of the world’s leading centres for numerical weather prediction, is operationally producing high-quality forecasts up to seasonal timescales. By inviting global participation, ECMWF seeks to discover how AI/ML techniques can complement traditional forecasting methods and address the challenges of sub-seasonal prediction. By leveraging open data and embracing diverse perspectives, it also seeks to foster new partnerships and share insights across the global community.
The AI Weather Quest is funded under the second phase of Destination Earth, supporting efforts to exploit recent breakthroughs in AI/ML for weather and climate and enhance the capabilities of the digital twins.
This competition is open to participants from international corporations, forecasting institutions, AI/ML experts, meteorologists, researchers, and students from diverse fields.
No prior experience in weather forecasting is required—just a passion for solving real-world challenges with AI/ML, and access to your own computational resources for developing and training models.
The AI Weather Quest is committed to equity and inclusivity, striving to create a level playing field where participants from different backgrounds and skill sets can compete and collaborate.
Join a competitive community of innovators who are passionate about harnessing AI for real-world applications. By participating in the AI Weather Quest, you will:
Test and advance your expertise in AI/ML models by tackling real-world challenges in weather forecasting.
Contribute to operational advancements in weather forecasting.
Build valuable connections with ECMWF and the vibrant global community working at the forefront of AI and weather forecasting.
Achieve global recognition for your groundbreaking contributions.