Professor Zeng’s research has centred on the exchanges of energy, water, and momentum among the land, atmosphere, and ocean that influence weather and climate. His work on land-surface processes and model parameterisation has contributed to the development of widely used representations of soil moisture, snow, evapotranspiration, and other hydrometeorological variables in weather and climate models. He has also developed observation-based datasets integrating in situ measurements, satellite remote sensing, and modelling, helping to strengthen links between observation and simulation in Earth system research. His research spans land–atmosphere interactions, nonlinear atmospheric dynamics, remote sensing of hydrometeorological variables, and more recently the application of machine learning and artificial intelligence to environmental data analysis. He co-founded a hydrometeorology graduate programme in the United States and has contributed to major international research initiatives, including the Global Precipitation Experiment and the Global Energy and Water Exchanges project of the World Climate Research Programme. He has published more than 290 peer-reviewed papers and has served as a visiting or advisory scientist for major atmospheric and Earth science institutions. Through sustained interdisciplinary research and international scientific collaboration, Professor Zeng has contributed to advances in hydrometeorology, observation–model integration, precipitation prediction, and understanding of the coupled Earth system.