Yi-Chi Wang

Fields of work

My research focuses on convection processes and their interactions with climate variability and extreme events, emphasizing multi-scale dynamics and improving climate simulations. I use dynamical models, observations, and reanalysis to investigate how extreme events—such as heatwaves, cold surges, and heavy precipitation—are influenced by large-scale climate variability. By integrating climate simulations with statistical and machine learning methods, I provide scientifically robust regional climate information using rigorous, science-based methodologies. My work bridges the gap between large-scale climate drivers and local-scale responses, enhancing extreme event predictability and supporting climate adaptation.

Research interests

Multi-Scale Interactions in Convection, Extreme Weather, and Climate Variability

• Investigating how convection processes interact with large-scale climate drivers (e.g., NAO, Pacific-Japan pattern, ENSO) to influence regional extreme events such as heatwaves, cold surges, and heavy precipitation.
• Improving convection and precipitation processes in climate models to enhance extreme event predictability, particularly under changing climate conditions.
• Assessing the changes in frequency and intensity of extreme events (e.g., heatwaves, extreme rainfall) across diverse climate regimes and regions to inform climate adaptation.

Regional Climate Modeling and Downscaling

• Developing event-based downscaling frameworks to improve the simulation and predictability of extreme climatic events in high-resolution climate models.
• Enhancing dynamical downscaling capabilities in regional climate model to refine high-resolution climate projections for extreme weather events.
• Integrating machine learning techniques to improve downscaling accuracy and enhance regional climate risk assessments.

Special competences

• Multi-Scale Climate Dynamics – Expertise in convection processes, extreme weather events, and their interactions with large-scale climate variability.
• Climate Model Development – Extensive experience with climate models, focusing on convection representation and improving high-resolution climate simulations.
• Climate Data Analysis & Teleconnections Analysis – Investigating how ENSO, NAO, and the Pacific-Japan pattern influence regional climate variability and extreme events using reanalysis data, observational datasets, and statistical modeling.
• Machine Learning Applications in Climate Science – Applying deep learning techniques to improve climate downscaling and understand climate processes with climate simulations.

Latest publications

• Tsai, C.-T., Y.-C. Wang, W.-L. Tseng*, L.-C. Chiang (2024): Pacific Meridional Mode implicated as a prime driver of decadal summer temperature variation over Taiwan. Accepted by Journal of Climate.
• W.-L. Tseng, C.-W. Lin, Y.-C. Wang, K.-M. Chiu, Y.-S. Wu, Y.-H. Hsieh, Y.-T. Chen, H.-H. Hsu* (2024): Evaluating constraints on offshore wind farm installation across the Taiwan Strait by exploring the influence of El Niño-Southern Oscillation on weather window assessment. Heliyon, Vol.10, Issue 21, e40125. doi: 10.1016/j.heliyon.2024.e40125.

• Tseng, W.-L., Y.-C. Wang, H.-Y. Tseng, H.-H. Hsu, Y.-C. Chen* (2024): Compound Spatial Extremes of Heatwaves and Downstream Air Pollution Events in East Asia. Vol.312, 107772. doi: 10.1016/j.atmosres.2024.107772.