Hong Kong University of Science and Technology (Dr. Mengqian Lu, cemlu@ust.hk)

Next-Gen Climate Intelligence: AI-Physics Hybrid Subseasonal-to-Seasonal Prediction and Services is a cutting-edge initiative designed to support industries that rely on accurate and timely weather and climate information. By integrating advanced artificial intelligence with a proprietary dynamic climate model, this project delivers high-resolution forecasts spanning from near real-time to two months ahead. It serves key sectors such as offshore wind energy, aviation, maritime operations, agriculture, and the low-altitude economy.

The hybrid prediction system provides reliable and actionable insights, enhancing operational efficiency, safety, and sustainability. It empowers industries to optimize resource allocation, anticipate disruptions, and make informed decisions that drive long-term resilience and growth. Whether it’s boosting wind farm productivity, reducing maritime navigation risks, or improving disaster preparedness, this next-generation climate intelligence framework plays a vital role in helping industries adapt to an increasingly dynamic environmental landscape.

Institute of Atmospheric Physics, Chinese Academy of Science (Dr. Qing Bao baoqing@mail.iap.ac.cn), collaborate with Department of Hydrology and Meteorology, Nepal (Ms. Pokharel Bibhuti, bibhel@gmail.com), Department of Local Governance & Disaster Management, Bhutan (Mr. Tshewang Sonam, stshewang@moha.gov.bt), The International Centre for Integrated Mountain Development (ICIMOD)(Dr. RongKun Liu, Rongkun.Liu@icimod.org)

This application belongs to a project in collaboration with the International Centre for Integrated Mountain Development (ICIMOD), aligning with the first main direction of funding as outlined in the guidelines.

In the context of global climate change, many countries in the southern slopes of the Himalayan region, such as Nepal, Bhutan, frequently encounter extreme rainfall events during the summer, which can easily trigger secondary disasters like floods and landslides, posing a serious threat to life safety, agricultural production, and infrastructure. Combining the scientific research strengths of both domestic and international parties, this project will identify the temporal peaks and spatial areas of extreme rainfall on the southern slopes of the Himalayan region at the monthly scale, based on in situ observation data, multi-source integrated precipitation, and reanalysis data. We will discern their characteristics and corresponding mechanisms. Furthermore, based on the IAP-CAS (Institute of Atmospheric Physics, Chinese Academy of Sciences) sub-seasonal dynamic prediction system, an assessment will be made of the extreme rainfall prediction skills on the southern slopes of the Himalayan region in the multi-year hindcast. Then, we will analyze the monthly prediction errors of extreme rainfall and explore the potential physical reasons of prediction errors in conjunction with the mechanisms of monthly extreme rainfall variations. Finally, considering the topography on the study region, which significantly affects summer rainfall, we will optimize the topographic related parameters of the prediction system to enhance the prediction skills for extreme rainfall at the sub-seasonal scale. Then we achieve real-time forecasting and propose appropriate response measures for corresponding countries.

The outcomes of this project will contribute to enhancing the extreme rainfall prediction capabilities in ecologically fragile areas, providing disaster warning information to relevant countries, and strengthening the resilience to climate-related disasters. Meanwhile, under the guidance of this project, the research capabilities of personnel from relevant institutions in partner countries will be enhanced.

Zhejiang University, China (Dr. Xiaojing Jia, jiaxiaojing@zju.edu.cn), collaborate with Pakistan Meteorological Department (Dr. Furrukh Bashir, furrukhbashir@arizona.edu), Department of Hydrology and Meteorology, Nepal (Sudarshan Humagain), ICIMOD (Dr. RongKun Liu, Rongkun.Liu@icimod.org)

Human health in the Southern Slopes of the Central and Western Himalayas is increasingly threatened by frequent humid heatwaves. There is an urgent need to enhance heatwave risk management through advanced monitoring, accurate forecasting, and early warning systems.

This project aims to:
1. Develop a high-resolution heatwave hazard atlas by integrating multi-source observational data with downscaling techniques.
2. Identify precursor signals of heatwaves and unravel the combined effects of various Earth system components on heatwave.
3. Create an intelligent, high-resolution seasonal prediction model to improve disaster warning capabilities.

Additionally, the project seeks to strengthen regional collaboration among Belt and Road partners via data sharing, knowledge exchange, technical cooperation, and capacity building. These efforts will enhance climate resilience and promote sustainable development in the Southern Slopes of the Central and Western Himalayas.

Liu FC, Jia, X.J., Dong W, Renguang Wu, 2025, Evaluation of historical snow cover over the Tibetan Plateau in CMIP6, Advances in Atmospheric Sciences, doi: 10.1007/s00376-024-4212-9.

Qing Bao; (Institute of Atmospheric Physics, Chinese Academy of Science)

Guoxiong Wu

The ANSO-MISSPAD project, led by the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP-CAS), in collaboration with the Chinese Academy of Meteorological Sciences (CAMS) and Beijing Normal University, has triumphantly established a comprehensive medium-to-long-range weather-climate prediction network spanning countries along the Belt and Road Initiative (BRI). This network has markedly enhanced regional capabilities in mitigating meteorological disasters and fortified China's influence within the region.

The project focused on the development of the multi-model ensemble Seasonal-to-Subseasonal (S2S) Prediction System (CAS-S2S) and the MISSPAD service platform, which have been successfully implemented and promoted in countries such as Nepal and Sri Lanka. Notably, the CAS-S2S system has been integrated into the World Meteorological Organization's (WMO) Subseasonal-to-Seasonal Prediction Project, earning high praise and gratitude from the WMO. This system provides real-time and precise services to multiple national meteorological departments, offering predictive data on high-impact weather events to global users. The project has achieved remarkable success in international cooperation, talent cultivation, and societal benefits.

Beijing Normal University, China (Prof. Jing Yang, yangjing@bnu.edu.cn), collaborate with Prof. Peyman Mahmoudi, p_mahmoudi@gep.usb.ac.ir.

Agriculture plays a significant role in the national economy of Central Southwest Asia. The frequent drought in this region has seriously restricted the utilization of water resources, agricultural production, and socio-economic development. It is urgently needed to construct an effective seasonal prediction and early warning system for regional agricultural drought risk in Central Southwest Asia. According to the research advantages and complementary characteristics of both sides, this project is initially proposed to establish and evaluate a dynamic re-forecast database of the drought index for Central Southwest Asia by conducting seasonal (leading three months) hindcast experiments for the historical period using dynamic climate models. On this basis, using the re-forecast data and observation data, a statistical-dynamical seasonal prediction model for drought in Central Southwest Asia is expected to be designed subsequently by examining various machine learning algorithms involving the physical mechanism of drought formation. Ultimately, an early warning platform for agricultural drought risk in Central Southwest Asia could be constructed and offers real-time forecasting services, combined with the drought vulnerability model for crops by region, based on the satellite-derived vegetation phenological parameters, and the statistical-dynamic seasonal prediction results. The research is of great scientific significance and application value as it is conducive to scientific disaster prevention and mitigation, ensuring agricultural production, and facilitating regional sustainable development.

Beijing Normal University (Dr. Shuiqing Yin, yinshuiqing@bnu.edu.cn), collaborate with Tanzania Commission for Science and Technology (COSTECH) (Chief Research Officer, Dr. Philbert Modest Luhunga, philbert.luhunga@costech.or.tz ).

Prof. Xiaodan Guan; CO-PI: Prof.Jun Jian collaborative with Water Resources Research Institute, National Water Research Center, Egypt (Deputy Director, Dr. Doaa Amin, doaa_amin74@yahoo.com)

The Nile River is the lifeline for Egypt as it covers about 95% of its demand. The upstream developments in the Nile Basin countries affect the water share of Egypt. In addition, the natural Nile flows are very sensitive to relatively small changes in rainfall that may lead to a series of drought years. The Nile flow is very sensitive to any slight change in rainfall; therefore, it is highly vulnerable to climatic changes. Our knowledge regarding how large-scale climate patterns affect hydrologic extremes (droughts/floods) is not overly extensive. While floods and hurricanes have rapid beginnings, endings, and devastating effects on infrastructure, drought is a slow-onset condition that develops over a season or even years. Drought studies in recent years have revealed an increase in the occurrence and severity of droughts in some areas due to climate variability and climate change. Where precipitation and temperature changes should be used to create more adapted applications and information indicating the corresponding impacts on water supply especially in light of development projects and the construction of dams on the upper Nile, which may affect the extension of drought periods.

One of the most severe drought events, which occurred in the 80s, made the level of the High Aswan Dam (HAD) drop to the lowest level that can release water. At that time, the lake Nasser storage protected the people of Egypt from a certain famine. Many studies reported the high variability of the flow of the Nile due to colossal climatic oscillations. Additionally, the new development plans that Ethiopia started on the Blue Nile, will put the storage of Lake Nasser in an unclear condition with significant possibilities of a decrease in the storage volume. Accordingly, it is exceedingly crucial to know if the system has the necessary flexibility to meet Egypt’s demands in periods of prolonged drought.

In this research project, the Nile Model in the RiverWare software will be upgraded by extend the Nile schematic inside Egypt and continue downstream High Aswan Dam (HAD) until the Mediterranean Sea in the North. The new schematic will take into account; all infrastructures and their operation rules, the channels and drains network and the agricultural areas and their demands. Model outputs under different Shared Socioeconomic Pathway (SSP) scenarios (including SSP1-2.6, SSP2-4.5, SSP4-6.0, and SSP5-8.5) in CMIP6, and different projection methods (e.g. rank-based weighting method, emergence constraints, observational constraints) will feed the new Nile model in order to study the impact of climate change and the Grand Ethiopian Resilience Dam’s (GERD) operational scenarios on the flow to HAD during the drought periods. The drought assessment depends on drought indices will be studied during the project to classify the risk degree on the Egypt water share. In addition, the project will provide different scenarios for HAD operation that cope with the expected water shortage from the different scenarios of the GERD operation and the change in climate.

The project will provide fruitful research cooperation between both Egyptian and Chinese researchers, in addition to transferring expertise and technology from the Chinese side to the Egyptian side. This cooperation will result in the development of optimal and flexible operating policies that can adapt to climate change and any future variables that may threaten water security in order to reach effective water management in accordance with the development strategy towards Egypt’s Vision 2030.

Dr. Ke Wei (Institute of Atmospheric Physics, Chinese Academy of Science), weike@mail.iap.ac.cn

Dr. Andrey V. Koval (Saint-Petersburg State University), a.v.koval@spbu.ru

The latest data reveals that global warming has become a fact and shows the characteristics of accelerating. However, severe cold episodes and snowstorm events have also occurred frequently in the past 10-20 years, affecting the most populated regions of East Asia, North America, and Europe, leading to severe economic losses and casualties. The cooling trend also occurs over large areas of Eurasia and North America continents in specific seasons and regions. This has also caused confusion about global warming and its adaptation policies. Presently, the current theories of sea ice and air-sea interaction can not fully explain the occurrence of cold events. This study explores the mechanism of extreme cold events and cold trends in the northern hemisphere under the background of global warming through the discussion of dynamic processes in the atmosphere, especially the analysis of stratosphere-troposphere interaction and planetary wave activity anomalies.The project's research results will enrich the conception of global warming and contribute to the understanding of regional climate change and provide a basis for disaster prevention and mitigation and climate change adaptation in the pan-Arctic and Eurasia regions.

Qing Bao; (Institute of Atmospheric Physics, Chinese Academy of Science)

The Madden-Julian Oscillation (MJO) plays a critical role in regulating global weather and climate variability by triggering and maintaining extreme events. Therefore, accurate forecasting of MJO is crucial for predicting extreme weather events and short-term climate prediction. It is also one of the core indicators of Subseasonal to Seasonal (S2S) prediction. This project aims to extract characteristic models from the multi-year hindcast from an S2S prediction system of IAP-CAS. The project will apply the second-order exact sampling method to establish an S2S ensemble forecast initialization module. The ensemble sensitivity experiments and operational predictions of the MJO will be carried out. This project focuses on the ability of the ensemble initial field generated by the second-order exact sampling method to predict the precursor signals and thermos dynamic processes generated by MJO convection. The final goal is to improve the MJO prediction skills in the S2S system of IAP-CAS by optimizing the ensemble method.

Nanjing University of Information Science & Technology

The project aims at assessing the risk of water resource shortage in the Indus River Basin, South Asia. It seeks to enhance the understanding of the basin's water carrying capacity and identify climatic tipping points that may lead to severe water shortages, thereby providing support for integrated river basin management and sustainable development of the transboundary river basin.

Qing Bao; (Institute of Atmospheric Physics, Chinese Academy of Science)

Improving the Subseasonal to Seasonal (S2S) prediction skills, especially improving the skills of extreme events, is of vital importance to our national disaster reduction and sustainable development. This project is planned to be based on an operational S2S prediction system, applying the globally variable-resolution stretched-grid method to achieve downscaling. Focus on the ability of stretched grids to predict extreme events, and achieve the goal of improving the S2S skills in China. Research contents: ① Relying on the heterogeneous supercomputer and applying the variable-resolution stretched-grid model to establish an ensemble S2S downscaling prediction system with a resolution of ~12 kilometers in China; ② Complete hindcast, carry out real-time prediction, publicly release results, and verify the ability to predict extreme events in China and analyze possible causes; ③ Carry out sensitivity tests of the stretched-grid model on boundary processing methods, resolution and key physical processes, and explore the impact on extreme event prediction techniques.

Institute of Atmospheric Physics, Chinese Academy of Science, China (Dr. Anmin Duan, amduan@lasg.iap.ac.cn), Co-PI Thai Meteorological Department (Deputy Director-General, Miss Kornrawee Sitthichivapak, kornrawee_s@hotmail.com)

The project proposes to investigate the characteristics, mechanism, and future projection of the extreme rainfall and associated flood events on interdecadal timescale over Indochina peninsula and southern China (INCSC) region. The proposed study will be conducted with four objectives. (a) Explore the characteristics and physical mechanisms of the interdecadal variations in extreme rainfall events over INCSC region under the background of global warming; (b) Analyze the simulation ability and deviation reasonof China new generation climate system model CAS-FGALS-F toward INCSC extreme rainfall events on interdecadal time scale and ; (c) Project the future (2025-2100) INCSC extreme rainfall events per scenario with the aid of the multi-model simulations provided by the international Coupled Model Intercomparison Project Phase 6 (CMIP6); (d) Study and develop a flood forecasting model adaptive to INCSC region based on DRIVE land and hydrology process model, and compile it into the network flood disseminate platform. The project is expected to substantially increase our acknowledge for the characteristics and physical mechanism of the interdecadal variation in extreme rainfall and associated flood events over INCSC region, and to provide information for improving the climate system model's simulation skill of extreme rainfall events and flood forecasting over this region. It is also expected to enhance academic cooperation, cultivate talents and contribute high-quality scientific papers jointly by Chinese and Thai scientists.