Analysis and modeling of statistical distributions of indoor radon concentration from data valorization to mapping and simulations are critical issues for real decision-making processes. The usual way to model indoor radon concentrations is to assume lognormal distributions of concentrations on a given territory. While these distributions usually model correctly the main body of the data density, they cannot model the extreme values, which are more important for risk assessment. In this paper, global and local indoor radon distributions are modeled using Extreme Value Theory (EVT). Emphasis is put on the tails of the distributions and their deviations from lognormality. The best fits of distributions to real data set density have been computed and goodness of fit with Root Mean Squared Error (RMSE) is evaluated. The results show that EVT performs better than lognormal pdf for real data sets characterized by high indoor radon concentrations.