We developed a new global daily dataset of turbulent heat exchanges between the ocean and atmosphere from 1993 to 2017. Utilizing a novel approach that combines machine learning with physical constraints, our model generates more accurate and physically reasonable estimates compared to existing datasets. This advancement enables improved understanding of ocean-atmosphere interactions, which are crucial for monitoring Earth's energy and water cycles and enhancing climate change projections.