Effective climate risk assessment is hindered by the resolution gap between coarse global climate models and the fine-scale information needed for regional decisions. We introduce GenFocal, an AI framework that generates statistically accurate, fine-scale weather from coarse climate projections, without requiring paired simulated and observed events during training. GenFocal synthesizes complex and long-lived hazards, such as heat waves and tropical cyclones, even when they are not well represented in the coarse climate projections. It also samples high-impact, rare events more accurately than leading methods. By translating large-scale climate projections into actionable, localized information, GenFocal provides a powerful new paradigm to improve climate adaptation and resilience strategies.
Generating High-Resolution Regional Precipitation Using Conditional Diffusion Model
School of Computing, KAIST（韩国科学技术院计算机学院）；Data Science Group, IBS（基础科学研究院数据科学组）；Department of Climate System, PNU（釜山大学气候系统系）；Center for Climate Physics, IBS（基础科学研究院气候物理中心）
MERCURY: A fast and versatile multi-resolution based global emulator of compound climate hazards
University of Oxford（牛津大学）；Montreal Polytechnique（蒙特利尔理工学院）；Columbia Climate School（哥伦比亚气候学院）；International Institute for Applied Systems Analysis（国际应用系统分析研究所）；Universität Leipzig（莱比锡大学）；IRI THESys；Humboldt-Universität zu Berlin（柏林洪堡大学）；Institute Pierre-Simone-Laplace（皮埃尔-西蒙·拉普拉斯研究所）
Institute of Research and Development, Duy Tan University（大越大学研究与发展学院）；Obuda University（欧布达大学）；Oxford Brookes University（牛津布鲁克斯大学）；Ton Duc Thang University（同塔大学）；Hong Kong Polytechnic University（香港理工大学）
Climate change impacts and adaptations are the subjects to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken under consideration. An ANFIS based post-processing technique was employed to match the power output…
Machine Learning based Parameter Sensitivity of Regional Climate Models -- A Case Study of the WRF Model for Heat Extremes over Southeast Australia
Commonwealth Scientific and Industrial Research Organisation（联邦科学与工业研究组织）；Massachusetts Institute of Technology（麻省理工学院）；TU Delft（代尔夫特理工大学）；Harry Butler Institute, Murdoch University（默多克大学哈里·巴特勒研究所）；University of New South Wales（新南威尔士大学）；NSW Bushfire and Natural Hazards Research Centre（新南威尔士州丛林大火与自然灾害研究中心）
Selection of CMIP6 Models for Regional Precipitation Projection and Climate Change Assessment in the Jhelum and Chenab River Basins
Department of Computer Science, University of Évora（埃夫拉尔大学计算机科学系）；MED—Mediterranean Institute for Agriculture, Environment and Development & CHANGE—Global Change and Sustainability Institute, Institute for Advanced Studies and Research, University of Évora（地中海农业、环境与发展研究所及全球变化与可持续性研究所，埃夫拉尔大学高级研究学院）；NUST Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST)（国家科技大学土木工程研究所（NICE）、土木与环境工程学院（SCEE））；Institute of Geographical Information Systems (IGIS), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST)（地理信息系统研究所（IGIS）、土木与环境工程学院（SCEE），国家科技大学）；IMT Atlantique, Lab-STICC UMR CNRS 6285（IMT Atlantic、Lab-STICC UMR CNRS 6285）
Johannes Kepler University Linz（林茨约翰内斯·开普勒大学）；University of Alabama（阿拉巴马大学）
Climate change affects occurrences of floods and droughts worldwide. However, predicting climate impacts over individual watersheds is difficult, primarily because accurate hydrological forecasts require models that are calibrated to past data. In this work we present a large-scale LSTM-based modeling approach that -- by training on large data sets -- learns a diversity of hydrological behaviors. Previous work shows that this model is more accurate than current state-of-the-art models, even when the LSTM-based approach operates out-of-sample and the latter in-sample. In this work, we show how …
Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction
Nanjing University of Information Science and Technology（南京信息工程大学）；Japan Agency for Marine-Earth Science and Technology（日本海洋研究开发机构）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）；Tokyo Institute of Technology（东京工业大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所革新智能综合研究中心）
Nowadays, agricultural field is experiencing problems related to climate change that result in the changing patterns in cropping season, especially for paddy and coarse grains, pulses roots and Tuber (CGPRT/Palawija) crops. The cropping patterns of rice and CGPRT crops highly depend on the availability of rainfall throughout the year. The changing and shifting of the rainy season result in the changing cropping seasons. It is important to find out the cropping patterns of paddy and CGPRT crops based on monthly rainfall pattern in every area. The Oldeman's method which is usually used in the cl…
Multi-variable Hard Physical Constraints for Climate Model Downscaling
Instituto de Física de Cantabria (IFCA), CSIC-UC（坎塔布里亚物理研究所（IFCA），西班牙国家研究委员会-坎塔布里亚大学）；Mila Quebec AI Institute（米拉魁北克人工智能研究所）；University of Montreal（蒙特利尔大学）；Fraunhofer ITWM（弗劳恩霍夫工业数学研究所）；University of Kaiserslautern（凯泽斯劳滕大学）；McGill University（麦吉尔大学）
Learning Regional Monsoon Patterns with a Multimodal Attention U-Net
1Computer Science \& Engineering, Indian Institute of Technology Roorkee, India 2Computer Science \& Engineering, Indian Institute of Technology Ropar, India 3 Computer Science \& Engineering, Indian Institute of Technology (BHU) Varanasi, India
