The heavy-tailed nature of precipitation intensity impedes precise precipitation nowcasting. Standard models that optimize pixel-wise losses are prone to regression-to-the-mean bias, which blurs extreme values. Existing Fourier-based methods also lack the spatial localization needed to resolve transient convective cells. To overcome these intrinsic limitations, we propose WADEPre, a wavelet-based decomposition model for extreme precipitation that transitions the modeling into the wavelet domain. By leveraging the Discrete Wavelet Transform for explicit decomposition, WADEPre employs a dual-branch architecture: an Approximation Network to model stable, low-frequency advection, isolating deterministic trends from statistical bias, and a spatially localized Detail Network to capture high-frequency stochastic convection, resolving transient singularities and preserving sharp boundaries. A subsequent Refiner module then dynamically reconstructs these decoupled multi-scale components into the final high-fidelity forecast. To address optimization instability, we introduce a multi-scale curriculum learning strategy that progressively shifts supervision from coarse scales to fine-grained details. Extensive experiments on the SEVIR and Shanghai Radar datasets demonstrate that WADEPre achieves state-of-the-art performance, yielding significant improvements in capturing extreme thresholds and maintaining structural fidelity. Our code is available at https://github.com/sonderlau/WADEPre.
How Effective Are Time-Series Models for Precipitation Nowcasting? A Comprehensive Benchmark for GNSS-based Precipitation Nowcasting
Sanya Science and Education Innovation Park, Wuhan University of Technology（武汉理工大学三亚科技创新园）；Wuhan University of Technology（武汉理工大学）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；Interdisciplinary Artificial Intelligence Research Institute, Wuhan College（武汉学院交叉人工智能研究院）；Shanghai Artificial Intelligence Laboratory, Shanghai（上海人工智能实验室）；School of Earth and Space Science and Technology, Wuhan University（武汉大学地球空间科学与技术学院）；School of Water Resources and Hydropower Engineering, Wuhan University（武汉大学水利水电学院）；GNSS Research Center, Wuhan University（武汉大学GNSS研究中心）
Exploring Geometric Deep Learning For Precipitation Nowcasting
Technical University of Munich (TUM)（慕尼黑工业大学）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Indian Institute of Technology Delhi（印度理工学院德里分校）
MAG-Net: Radar-Satellite Fusion for Severe Convective Nowcasting with Matched Evaluation of Satellite Contributions
Chinese Academy of Meteorological Sciences（中国气象局）；National Meteorological Information Center（国家气象信息中心）；Xiong’an Institute of Meteorological Artificial Intelligence（雄安气象人工智能研究院）
StormDiT: A generative AI model bridges the 2-6 hour 'gray zone' in precipitation nowcasting
Shanghai Typhoon Institute（上海台风研究所）；Key Laboratory of Numerical Modeling for Tropical Cyclone of the China Meteorological Administration（中国气象局热带气旋数值模拟重点实验室）；State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所大气边界层物理与大气化学国家重点实验室）；College of Earth and Planetary Sciences, University of Chinese Academy of Sciences（中国科学院大学地球与行星科学学院）；Earth System Modeling and Prediction Center, China Meteorological Administration（中国气象局地球系统模式与预测中心）；State Key Laboratory of Severe Weather Meteorological Science and Technology（severe weather meteorological science and technology 国家重点实验室）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology（南京信息工程大学气象灾害教育部重点实验室/气候变化与环境国际合作联合实验室/气象灾害预报评估与协同创新中心）
Nowcast3D: Reliable precipitation nowcasting via gray-box learning
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学光明学院人工智能学院）；Earth System Modeling and Prediction Center, China Meteorological Administration（中国气象局地球系统建模与预测中心）；Shanghai Typhoon Institute, China Meteorological Administration（中国气象局上海台风研究所）；School of Atmospheric Physics, Nanjing University of Information Science and Technology（南京信息工程大学大气物理学院）；Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）；College of Earth and Planetary Sciences, University of Chinese Academy of Sciences（中国科学院大学地球与行星科学学院）；Chinese Academy of Meteorological Sciences, China Meteorological Administration（中国气象科学研究院）；School of Engineering Science, University of Chinese Academy of Sciences（中国科学院大学工程科学学院）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大型模型与智能治理研究重点实验室）；State Key Laboratory of Severe Weather Meteorological Science and Technology（severe weather meteorological science and technology state key laboratory）；Laboratory of Climate Change Mitigation and Carbon Neutrality, Henan University（河南省气候变化缓解与碳中和实验室）
Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas
Instituto de Física de Cantabria (IFCA), CSIC-UC（坎塔布里亚物理研究所（CSIC-UC联合机构））；MicroStep-MIS（MicroStep-MIS公司）；Scientific Computing Center (SCC), Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院科学计算中心）
Continuous Flood Nowcasting in South Asia: A Multi-Sensor Ensemble Remote Sensing Framework for Flood Extent
Planetary Health Informatics (PHI) Lab, University of Oxford（行星健康信息学（PHI）实验室，牛津大学）；Blavatnik School of Government, University of Oxford（布劳维克政府学院，牛津大学）；PMIU Secretariat, Irrigation Department, Pakistan（灌溉部门PMIU秘书处，巴基斯坦）
