Oxygen isotopes in tree rings ($\delta$$^{18}$O$_{TR}$) are widely used to reconstruct past climates. However, the complexity of climatic and biological processes controlling isotopic fractionation is not yet fully understood. Here, we use the MAIDENiso model to decipher the variability in $\delta$$^{18}$O$_{TR}$ of two temperature-sensitive species of relevant palaeoclimato-logical interest ($Picea\ mariana$ and $Nothofagus\ pumilio$) and growing at cold high latitudes in North and South America. In this first modelling study on $\delta$$^{18}$O$_{TR}$ values in both northeastern Canada (53.86 • N) and western Argentina (41.10 • S), we specifically aim at (1) evaluating the predictive skill of MAIDENiso to simulate $\delta$$^{18}$O$_{TR}$ values, (2) identifying the physical processes controlling $\delta$$^{18}$O$_{TR}$ by mechanistic modelling and (3) defining the origin of the temperature signal recorded in the two species. Although the linear regression models used here to predict daily $\delta$$^{18}$O of precipitation ($\delta$$^{18}$O$_P$) may need to be improved in the future, the resulting daily $\delta$$^{18}$O$_P$ values adequately reproduce observed (from weather stations) and simulated (by global circulation model) $\delta$$^{18}$O$_P$ series. The $\delta$$^{18}$O$_{TR}$ values of the two species are correctly simulated using the $\delta$$^{18}$O$_P$ estimation as MAIDENiso input, although some offset in mean $\delta$$^{18}$O$_{TR}$ levels is observed for the South American site. For both species, the variability in $\delta$$^{18}$O$_{TR}$ series is primarily linked to the effect of temperature on isotopic enrichment of the leaf water. We show that MAIDENiso is a powerful tool for investigating isotopic fractionation processes but that the lack of a denser isotope-enabled monitoring network recording oxygen fractionation in the soil–vegetation–atmosphere compartments limits our capacity to decipher the processes at play. This study proves that the eco-physiological modelling of $\delta$$^{18}$O$_{TR}$ values is necessary to interpret the recorded climate signal more reliably.