The cooling efficiency of the land surface, i.e., its ability to dissipate absorbed radiation and moderate temperature rise, is reflected in its apparent heat capacity, a property that varies throughout the day in response to the relative intensities of sensible and latent heat fluxes. Under clear-sky conditions, the daytime increase in apparent heat capacity can be reliably estimated using geostationary satellite data and used to derive a new dataset called Cooling Efficiency Factor Index (CEFI). This index quantifies land surface energy dissipation through turbulent and ecohydrological processes from 2005 to near real time at a spatial resolution of 5 km. The spatial distribution of the CEFI dataset is primarily determined by land cover, water availability, surface roughness, and wind speed. Its temporal variability can be exploited to derive proxies for variables and processes that are otherwise difficult to observe, especially in real time, such as evapotranspiration and wind speed anomalies. Accordingly, the CEFI dataset can serve as an indicator of vegetation drought stress, the condition in which plants close their stomata due to soil water limitation and high atmospheric water demand, as well as vegetation productivity. It can also detect flash droughts and support improved estimation of fire risk in natural ecosystems, crop production losses in agricultural areas, and dust formation in desert regions. In addition, the dataset can be used to quantify the cooling efficiency of urban areas. This paper provides access to a publicly available CEFI dataset updated in near real time. Given its broad range of applications, the dataset can be used for both research and operational monitoring, to constrain poorly observed processes in dynamical models, and as an additional predictor or predictand in machine learning applications.