This study presents high-resolution pantropical estimates of near-ground (15 cm above the surface) temperatures inside tropical forests, revealing spatial and temporal microclimate patterns. The results show that understory temperatures are on average 1.6°C cooler than open-air temperatures, with a diurnal temperature range 1.7°C lower. The study highlights substantial spatial variability in tropical forest microclimates, influenced by large-scale climate conditions, vegetation structure, and topography. These patterns cannot be captured by existing macroclimate grids. The findings contribute to understanding the actual thermal ranges experienced by organisms in tropical forests and provide insights into how these limits may be affected by climate change and ecosystem disturbances. Tropical forests host up to half of Earth's biodiversity. Temperature patterns are fundamental to defining the survival, growth, and reproduction of organisms, shaping species distribution and diversity. However, current climate datasets cannot accurately capture the range and variability of temperatures in tropical forests. Most products provide estimates of open-air temperatures, which differ from understory conditions. Forest structure is a key factor driving fine-scale horizontal and vertical variation in understory temperature. During clear-sky days, forest canopies reduce understory temperatures through absorption of solar radiation and evapotranspiration. At night, forest canopies retain outgoing longwave radiation, leading to warmer temperatures. Topographic factors such as elevation, slope, and aspect also influence microclimate patterns. Recent advances in remote sensing and big-data processing have renewed interest in microclimate ecology. This study used a machine learning model trained with in situ temperature data from 180 microclimate sensors across three continents. The model was driven by satellite observations of forest structural and functional traits, topographic variables, and macroclimatic conditions. The results show that understory temperatures vary significantly across continents, seasons, and time of day. The study also highlights the importance of biophysical and climatic variables in governing spatiotemporal behaviors of understory temperatures. The findings provide new insights into how microclimate influences species distribution and their response to climate change. The results emphasize the need for more accurate temperature data to better understand ecological processes in tropical forests.This study presents high-resolution pantropical estimates of near-ground (15 cm above the surface) temperatures inside tropical forests, revealing spatial and temporal microclimate patterns. The results show that understory temperatures are on average 1.6°C cooler than open-air temperatures, with a diurnal temperature range 1.7°C lower. The study highlights substantial spatial variability in tropical forest microclimates, influenced by large-scale climate conditions, vegetation structure, and topography. These patterns cannot be captured by existing macroclimate grids. The findings contribute to understanding the actual thermal ranges experienced by organisms in tropical forests and provide insights into how these limits may be affected by climate change and ecosystem disturbances. Tropical forests host up to half of Earth's biodiversity. Temperature patterns are fundamental to defining the survival, growth, and reproduction of organisms, shaping species distribution and diversity. However, current climate datasets cannot accurately capture the range and variability of temperatures in tropical forests. Most products provide estimates of open-air temperatures, which differ from understory conditions. Forest structure is a key factor driving fine-scale horizontal and vertical variation in understory temperature. During clear-sky days, forest canopies reduce understory temperatures through absorption of solar radiation and evapotranspiration. At night, forest canopies retain outgoing longwave radiation, leading to warmer temperatures. Topographic factors such as elevation, slope, and aspect also influence microclimate patterns. Recent advances in remote sensing and big-data processing have renewed interest in microclimate ecology. This study used a machine learning model trained with in situ temperature data from 180 microclimate sensors across three continents. The model was driven by satellite observations of forest structural and functional traits, topographic variables, and macroclimatic conditions. The results show that understory temperatures vary significantly across continents, seasons, and time of day. The study also highlights the importance of biophysical and climatic variables in governing spatiotemporal behaviors of understory temperatures. The findings provide new insights into how microclimate influences species distribution and their response to climate change. The results emphasize the need for more accurate temperature data to better understand ecological processes in tropical forests.