This study investigates multivariable and multiscalar climate-δ18O relationships, through the use of statistical modeling and simulation. Three simulations, of increasing complexity, are used to generate time series of daily precipitation δ18O. The first simulation uses a simple local predictor (daily rainfall amount). The second simulation uses the same local predictor plus a larger-scale climate variable (a daily NAO index), and the third simulation uses the same local and non-local predictors, but with varying seasonal effect. Since these simulations all operate at the daily timescale, they can be used to investigate the climate-δ18O patterns that arise at daily-interannual timescales. These simulations show that (1) complex links exist between climate-δ18O relationships at different timescales, (2) the short-timescale relationships that underlie monthly predictor-δ18O relationships can be recovered using only monthly δ18O and daily predictor variables, (3) a comparison between the simulations and observational data can elucidate the physical processes at work. The regression models developed are then applied to a 2-year dataset of monthly precipitation δ18O from Dublin and compared with event-scale data from the same site, which illustrates that the methodology works, and that the third regression model explains about 55% of the variance in δ18O at this site. The methodology introduced here can potentially be applied to historic monthly δ18O data, to better understand how multiple-integrated influences at short timescales give rise to climate-δ18O patterns at monthly-interannual timescales.