Land-atmosphere interactions (LAI) are critical processes in the Earth system, regulating energy, water, and carbon cycles and exerting significant feedback effects on regional and global climate. Traditional Earth system models (ESMs) rely on simplified parameterization schemes for LAI processes, leading to notable uncertainties in simulating surface energy balance and climate feedback. This s...

Land-atmosphere coupling processes, involving the exchange of energy, water, and momentum between the land surface and the atmosphere, are critical for regulating regional weather systems and extreme precipitation events. Traditional land-atmosphere coupling models rely on parameterization schemes that simplify complex nonlinear processes (e.g., soil moisture-evaporation feedback, vegetation-at...

Tropical cyclones (TCs) are severe marine meteorological disasters whose tracks are closely regulated by ocean-atmosphere coupling processes, such as sea surface temperature (SST) anomalies, latent heat flux exchange, and upper-ocean thermal structure. Traditional TC track prediction models, which rely on parameterized ocean-atmosphere coupling schemes or pure statistical learning methods, ofte...

Ocean-atmosphere coupling processes are core components of the Earth system, regulating global energy redistribution and regional extreme weather events, especially tropical cyclones (TCs). Traditional coupled ocean-atmosphere models rely on grid-based parameterization schemes to describe nonlinear coupling processes, leading to significant uncertainties in simulating key variables such as sea ...

The global hydrological cycle is a core component of the Earth system, and its response to climate change has profound impacts on ecological security and human society. Traditional hydrological models are limited by simplified parameterization schemes, leading to uncertainties in simulating complex hydrological processes. This study proposes a deep learning-enhanced global hydrological simulati...