In the analysis of most spatial and spatio-temporal processes in environmental studies, observations present skewed distributions, with a heavy right or left tail. Usually, a single transformation of the data is used to approximate normality, and stationary Gaussian processes are assumed to model the transformed data. Spatial interpolation and/or temporal prediction are routinely performed by transforming the predictions back to the original scale. The choice of a distribution for the data is key for spatial interpolation and temporal prediction. In this talk, I will start discussing the advantages and disadvantages of using a single transformation to model such processes. Then I will discuss some recent advances in the modeling of non-Gaussian spatial and spatio-temporal processes.