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.

Zhu, T., Lu, M., Yang, J*., Bao, Q., New, S., Pan, Y., Qu, A., Feng, X., Jian, J., Hu, S., & Pan, B. (2025). Enhancing Ready-to-Implementation subseasonal crop growth predictions in central Southwestern Asia: A machine learning-climate dynamical hybrid strategy. Agricultural and Forest Meteorology, 370, 110582. https://doi.org/10.1016/j.agrformet.2025.110582

Zhang, S., Yang, J*., Zhu, T., & Bao, Q. (2025). Interannual climate anomalies modulate the subseasonal dynamical prediction skill from the regional perspective over Central Southwest Asia. Atmospheric Research, 319, 108023. https://doi.org/10.1016/j.atmosres.2025.108023

Pan, Y., Yang, J*., Chen, D., Zhu, T., Bao, Q., & Mahmoudi, P. (2023). Skillful seasonal prediction of summer wildfires over Central Asia. Global and Planetary Change, 221, 104043. https://doi.org/10.1016/j.gloplacha.2023.104043

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.

Beijing Normal University, China (Prof. Tao Ye, yetao@bnu.edu.cn), collaborate with Dr. Tadesse Woldemariam Gole, twgole@gmail.com

Agricultural index insurance is considered as a powerful tool for smallholder producers in developing countries to transfer production risks and escape the poverty trap. However, failures to design high-quality product, to meet the urgent needs of small farmers, and to build an inclusive financial model around index insurance have limited the development of agricultural index insurance and its role in global poverty eradication. This project intends to carry out a comparative case study of coffee weather index insurance in Yunnan Province of China and Sidamo region of Ethiopia. It will identify the main driving factors of coffee yield loss, build prediction models for coffee yield loss and revenue loss, and design a series of coffee index insurance product schemes for yield protection and income protection. It will reveal the preference characteristics of coffee farmers to index insurance. It will carry out multi-criterion evaluation of the impact of products and supporting financial inclusion policies, and recommend a set of high-quality coffee index insurance scheme for pilot programs the local study areas. Through case comparison and experience summarization, the project will finally derive a paradigm for developing high-quality agricultural index insurance projects with "scientifically designed, preferred by farmers, and public-funds efficiently used". This will finally provide knowledge and experience to help more developing countries to successfully design and implement agricultural index insurance programs, and contribute to the realization of the Sustainable Development Goals of eradicating poverty and hunger globally.

The project used remote-sensing imageries and machine learning technology to map coffee distributions. These maps provide essential information on the geolocations of coffee plots for insurance underwriting.

A 10m-resolution coffee distribution map of Yunnan in 2023 was generated using an object-based non-iterative clustering algorithm and deep neural network classifier, based on Sentinel-2 imagery and SRTM data. This is the first non-commercial dataset for this region. A similar map for the Ethiopian study area is currently under development.

The project used both controlled experiments and statistical modeling to identify critical climatic factors damaging coffee yield and develop prediction models.

Control experiments were conducted for two consecutive years in the climate chamber of Yunnan University and the Baoshan field experimental station.

The experimental results indicated that:
- Chill stress during the maturity stage irreversibly damaged the top leaves, increasing flower bud and young fruit abortion rates in the following year.

- Moderate or severe soil drought during the flowering stage significantly reduced the numbers of coffee flowers and fruits and the yield. The net photosynthetic and transpiration rates decreased significantly as the degree of drought increased.

Coffee yield responses to key climate stressors were quantified using generalized additive models (GAMs) and random forest algorithms based on historical statistical yield and climate data, trained separately in Yunnan and Ethiopia.

The yield-climate response model trained for Yunnan showed that drought (VPD) during flowering and chill (TNn) during maturation had a significant, monotonically negative impact on coffee yield, whereas heat had no significant effect. For every 0.1kPa increase in VPD during flowering, the yield may decrease by 4%, and for every 1°C drop in TNn during maturation, the yield may decline by 19%.

A yield–climate response model was also developed for the Jimma and Sidama regions in Ethiopia.

The project uses household questionnaire surveys and discrete choice experiments together to reveal smallholder coffee farmers’ preference.
- Yunnan coffee farmers support weather index insurance, favoring low-premium, high-coverage products and income protection, with basis risk having a limited impact.
- Inclusive finance options positively affect farmers’ insurance uptake. Farmers preferred direct agricultural input subsidies, followed by premium subsidies, and least preferred preferential loan interest rates.

An index-based insurance product was designed for Yunnan based on the aforementioned yield-climate response relationship, using flowering-stage VPD and maturity-stage TNn as key indices. It provides insurance protection of up to 15,000 yuan/ha at an average premium rate of 4.5%. The product was filed by China Taiping General Insurance Co. Ltd., and approved by the Yunnan Provincial Government. A pilot program was launched in Simao, Pu'er, with the first policy covering 66.67 ha.

An IBICA product has also been preliminarily designed for the Jimma and Sidama regions of Ethiopia. Further efforts should be jointly devoted by the project team and Ethiopian local insurance partners for product design, filing, approval, and promotion campaigns, so that a pilot program could be launched for the 2025-2026 production season.

Sun, H., Zhang, F., Raza, S.T., Zhu, Y., Ye, T., Rong, L., & Chen, Z*. (2023). Three decades of shade trees improve soil organic carbon pools but not methane uptake in coffee systems. Journal of Environmental Management, 347: 119166.
Yang, T., Li, Z., Bai, Y., Liu, X., & Ye, T*. (2023). Residents’ preferences for rural housing disaster insurance attributes in central and western Tibet. International Journal of Disaster Risk Science.
Du, X., Zheng, W., & Yao, Y*. (2023). The peer effect in adverse selection: Evidence from the micro health insurance market in Pakistan. Journal of Risk and Insurance, 90: 1063-1100.
Zhu, Y., Liu, Y., Chen, Z., Li, M., Fan, L*., Zhang, M*. (2024). Assessing the climate change impacts on Coffee arabica cultivation regions in China. Theoretical and Applied Climatology.
Liu J., Wang, Y., & Yao, Y*. (2024). Anticipated benefit termination and health care consumption responses: Evidence from a quasi-experiment. Journal of Economic Behavior and Organization.
Dong X., Gao J., Jiang M., Tao Y., Chen X., Yang X., Wang L., Jiang D., Xiao Z., Bai X., He F*. (2024). The Identification and Characterization of WOX Family Genes in Coffea arabica Reveals Their Potential Roles in Somatic Embryogenesis and the Cold-Stress Response. International journal of molecular sciences, 25(23):13031.
Wang, X., Ye, T*., Fan, L., Liu, X., Zhang, M., Zhu, Y., Gole, T.W. (2025). Chill topped historical Arabica coffee yield loss among climate stressors in Yunnan, China, followed by drought. npj Natural Hazards, 2, 32.
Ye, T., Liu, X. Disaster Insurance. Beijing: Beijing Normal University Press, 2024.

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.