Many efforts to forecast ecological responses to climate change are based on air temperatures at coarse spatial (degrees) and temporal (months) resolutions, but animals respond to multiple aspects of the environment at scales of minutes and meters. We aim to improve ecological and evolutionary forecasts by providing computational and visualization tools to address these discrepancies. The TrenchR R package facilitates microclimate modelling to translate weather station data into the environmental conditions experienced by organisms and biophysical modelling to predict organismal body temperatures given the environmental conditions. Interactive visualizations explore organismal responses to environmental conditions. Several use biophysical modelling to explore body temperatures and regions of thermal stress for a variety of taxa. Another leverages a database of insect development traits to predict phenology. TrEnCh-ed includes interactive R Shiny applications and associated tutorials to allow students and others interested to explore the ecological and evolutionary impacts of climate change through interacting with data. A series of tutorials introduces graduate students and other researchers to biophysical ecology. We aim to improve ecological and evolutionary forecasting tools for education, policy, and research and welcome your ideas and input.