Corrosion of metal in soils has been intensively investigated in the past. However, a review of the published literature shows that there are challenges for researchers to accurately predict the corrosion growth of buried pipes with spatial and temporal variations. This paper intends to develop a methodology to predict the corrosion pit growth for buried pipelines by thoroughly considering the spatial and temporal variability of corrosion processes. The developed method integrates the corrosion science, conditional random field theory, stochastic process, and copula method into an interrelated simulation algorithm for generating corrosion pit growth fields. It is found in the paper that the shape parameter and rate parameter of the gamma process, as well as the correlation structure of corrosion processes, are not only time-variant but also exhibit spatial variability with time. It is also found that the developed method is much superior to other methods in terms of accuracy and effectiveness. The proposed method considers the correlation between corrosion processes in a long pipeline, which is more practical. It can be concluded that generating an accurate corrosion growth field fully requires consideration of the spatial and temporal variability of model parameters.