Subseasonal forecasts, which predict weather conditions from ten days to two months ahead, are emerging as a powerful tool in the fight against climate-related risks. Striking a balance between short-term precision and long-term planning, these forecasts hit a "sweet spot" for actionable, science-based decision-making. While traditional weather forecasts focus on the next few days, and seasonal outlooks stretch over several months, subseasonal predictions offer a timely middle ground — and one that could be transformative for both governments and industries, if communicated effectively.
A recent review and outlook paper published in Advances in Atmospheric Sciences on January 5 presents how scientists can bridge the gap between complex forecast data and real-world decisions to turn raw climate intelligence into life-saving, economy-boosting action.
"Subseasonal forecasting better balances accuracy and timeliness. It provides sufficiently detailed predictive information while allowing ample time for early warnings and effective decision-making," said Jing Yang, first author of the study and a professor at Beijing Normal University in Beijing, China.
The advantages are clear: businesses can optimize operations and reduce losses, while governments can better prepare for climate disasters, safeguarding lives and infrastructure. Yet despite its game-changing potential, subseasonal data often remains underutilized. Both the private and public sector may hesitate to make potentially costly decisions based on complex and uncertain forecasts.
"The core challenge is transforming professional meteorological forecast data into tailored, actionable decision-making information for different industries," said Mengqian Lu, corresponding author of the study and a professor at the Hong Kong University of Science and Technology in Hong Kong. "Establishing a standardized and transferrable 'supply-demand interaction framework' for climate services is vital, and it must be scalable across industries, avoid redundancy, and bring science directly into decision-making process".
The accuracy of subseasonal predictions depends on many variables. For dangerous weather patterns, heatwaves and wildfires can be predicted up to four weeks out, tropical cyclones two to three weeks out, and floods one to two weeks out. For sustainability efforts, subseasonal forecasts can be used to help manage crop yield, conserve water resources, predict wind and solar energy, and predict sea ice impacts on shipping. All of these different forecasts will include unique complexities that the end user may need help interpreting.
That's where decision-ready tools come in.
The study recommends new forms of "actionable outputs" — such as risk maps that combine forecast data with local infrastructure, population vulnerability, and resilience metrics. For instance: Wildfire risk maps could show not just the likelihood of fire, but the severity and confidence level of the forecast. Extreme rainfall maps could integrate rainfall intensity, frequency, and probability with flood-prone infrastructure and population density.
The goal is not just to predict risk — but to communicate it clearly and credibly.
Looking ahead, researchers hope to continue to develop this framework for the real world. "The ultimate goal is to achieve the real-world application of atmospheric science, transforming scientific and technological advancements into economic and social value. Against the backdrop of climate change, we aim to effectively enhance disaster prevention and mitigation efforts as well as optimize resource utilization, thereby developing actionable pathways to genuinely achieve sustainable development goals," said Lu.
The study also forms part of the UNESCO " International Decade of Sciences for Sustainable Development " (2024–2033) initiative and serves as a key starting point for the "Seamless Prediction and Services Program for Sustainable Natural and Human Environments" ( SEPRESS , 2025–2032). The SEPRESS program aims to achieve close integration between scientific research and practical applications through advanced and reliable seamless weather-climate prediction, thereby promoting the sustainable development of global natural and human environments.
Other contributors include Anling Liu, Yuxian Pan, Shentong Li, Xinyao Feng, Shiyu Zhang, and Lu Tang at Beijing Normal University; Tat Fan Cheng, Lun Dai, Wen Deng, and Lujia Zhang at the Hong Kong University of Science and Technology; Miaoni Gao and Han Li at Nanjing University of Information Science and Technology; Tao Zhu and Qing Bao at the Chinese Academy of Sciences; Andrew W. Robertson at Columbia University; Tsz-cheung Lee at Hong Kong Observatory; Frederic Vitart at the European Centre for Medium-Range Weather Forecasts; Ping Liang at the China Meteorological Administration; Jun Jian at the Dalian Maritime University; Linlin Pan at the China Electric Power Research Institute; Stacey New at Arizona State University; Lei Wang at State Key Laboratory of Disaster Prevention and Reduction for Power Grid; Qichao Yao at the Ministry of Emergency Management of China; Xiaolong Jia at the National Climate Center; Xi Liang at the Ministry of Natural Resources, and Yaochi Su at Jiangmen Meteorological Service.
