Fldgen provides a system and method for emulating earth system models (ESMs), including the internal variability typically found in ESM output. Output from fldgen is spatially resolved and produced at annual resolution. Fldgen v. 1.0 produced only a single variable as output and was restricted to variables, such as temperature, for which values are approximately normally distributed. Fldgen v 2.0 removes the normal distribution restriction, allowing us to simulate variables that are not well approximated by a normal distribution, such as precipitation. As in version 1, the algorithms in fldgen v. 2 are designed to preserve the correlation structure of the climate variables in space and time. Additionally, the algorithms in version 2 preserve the correlation structure between variables, so that if, for example, in some region above-normal temperature is associated with below-normal precipitation, then the fldgen output will reflect this. Version 2 also adds the ability to work with incomplete data, such as data that is provided only over land grid cells. We have shown that this method is able to capture well-known quasi-periodic oscillations, such as the El Niño phenomenon, provided that they are present in the ESM data used to train the model. Fldgen takes as input a collection of outputs from ESMs and analyzes them to learn the patterns of correlation described above. This process takes roughly 10-15 minutes on a midrange workstation, depending on the size of the input data. Once this training process is complete, the software may be used to generate outputs for any climate scenario desired (climate scenarios are specified by the future trajectory of global mean temperature), as many times as desired. Output generation takes just a few seconds for each realization.