Understanding how precipitation isotopes vary spatially and temporally is important for tracer applications. We tested how well month-to-month variations in precipitation δ¹⁸O and (δ²H were captured by sinusoidal cycles, and how well spatial variations in these seasonal cycles could be predicted, across Switzerland. Sine functions representing seasonal cycles in precipitation isotopes explained between 47% and 94% of the variance in monthly δ¹⁸O and (δ²H values at each monitoring site. A significant sinusoidal cycle was also observed in line-conditioned excess. We interpolated the amplitudes, phases, and offsets of these sine functions across the landscape, using multiple linear regression models based on site characteristics. These interpolated maps, here referred to as a sinusoidal isoscape, reproduced monthly observations with prediction errors that were smaller than or similar to those of other isoscapes. Sinusoidal isoscapes are likely broadly useful because they concisely describe seasonal isotopic behavior and can be estimated efficiently from sparse or irregular data.