Dendroclimatic research has long assumed a linear relationship between tree-ring increment and climate variables. However, ring width frequently underestimates extremely wet years, a phenomenon we refer to as ‘wet bias’. In this paper, we present statistical evidence for wet bias that is obscured by the assumption of linearity. To improve tree-ring-climate modeling, we take into account wet bias by introducing two modified linear regression models: a linear spline regression (LSR) and a likelihood-based wet bias adjusted linear regression (WBALR), in comparison with a quadratic regression (QR) model.Using gridded precipitation data and tree-ring indices of multiple species from various sites in Utah, both LSR and WBALR show a significant improvement over the linear regression model and out-perform QR in terms of in-sample R2 and out-of-sample MSE. This further shows that the wet bias emerges from nonlinearity of tree-ring chronologies in reconstructing precipitation. The pattern and extent of wet bias varies by species, by site, and by precipitation regime, making it difficult to generalize the mechanisms behind its cause. However, it is likely that dis-coupling between precipitation amounts (e.g., percent received as rain/snow or percent infiltrating the soil) and its availability to trees (e.g., root zone dynamics), is the primary mechanism driving wet bias.