Conditional sequential Gaussian simulations (sGs) have been applied for the first time to the study of soil diffuse degassing from different volcanic and nonvolcanic systems. The application regards five data sets of soil CO2 fluxes measured with the accumulation chamber methodology at the volcanic areas of Solfatara of Pozzuoli (Italy), Vesuvio cone (Italy), Nisyros (Greece), and Horseshoe Lake (California) and at the nonvolcanic degassing area of Poggio dell'Olivo (Italy). The sGs algorithm was used to generate 100 realizations of CO2 flux for each area. Probabilistic summaries of these simulations, together with the information given by probability plots, were used (1) to draw maps of the probability that CO2 fluxes exceed thresholds specific for a background flux, i.e., to define the probable extension of the degassing structures, (2) to calculate the total CO2 output, and (3) to quantify the uncertainty of the estimation. The results show that the sGs is a suitable tool to model soil diffuse degassing, producing realistic images of the distribution of the CO2 fluxes that honor the histogram and variogram of the original data. Moreover, the relation between the sample design and the uncertainty of estimation was investigated leading to an empirical relation between uncertainty and the sampling density that can be useful for the planning of future CO2 flux surveys.