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The use of statistical models to simulate or to predict stream water temperature is becoming an increasingly important tool in water resources and aquatic habitat management. This article provides an overview of the existing statistical water temperature models. Different models have been developed and used to analyze water temperature-environmental variables relationship. These are grouped into two major categories: deterministic and statistical/stochastic models. Generally, deterministic models require numerous input data (e.g., depth, amount of shading, wind velocity). Hence, they are more appropriate for analyzing different impact scenarios due to anthropogenic effects (e.g., presence of reservoirs, thermal pollution and deforestation). In contrast to the deterministic models, the main advantage of the statistical models is their relative simplicity and relative minimal data requirement. Parametric models such as linear and non-linear regression are popular methods often used for shorter time scales (e.g., daily, weekly). Ridge regression presents an advantage when the independent variables are highly correlated. The periodic models present advantages in dealing with seasonality that often exists in periodic time series. Non-parametric models (e.g., k-nearest neighbours, artificial neural networks) are better suited for analysis of nonlinear relationships between water temperature and environmental variables. Finally, advantages and disadvantages of existing models and studies are discussed.