Intertemporal choices constitute a significant topic of interest in both psychological and behavioral-economics research. While many studies focus on decisions with precisely known reward delivery times, real-world situations typically involve only an imprecise knowledge of these timings (i.e., the delivery times are ambiguous). The current study uses a large size dataset (sample size N > 669) consisting of both risky and intertemporal ambiguous and nonambiguous choices and aims (i) to clarify the relationship between probability-ambiguity and time-ambiguity effects on choice, and (ii) to evaluate different computational models (attribute-wise and integrated-value models) across risky and intertemporal choice domains using a drift-diffusion model (DDM) framework. Analysis of the choice data revealed a significant association: Individuals who were more averse to time ambiguity also exhibited a stronger aversion to probability ambiguity, as indicated by a correlation of r = .28. The DDM analyses revealed that (i) DDMs incorporating ambiguity preferences outperformed models without ambiguity preferences in both the time and probability domain for most participants. Interestingly, (ii) while time-ambiguity aversion was best explained by an attribute-wise model, probability-ambiguity aversion was best explained by an integrated-value model. Finally, we found that (iii) if an individual's intertemporal decisions were best explained by a DDM incorporating ambiguity, then their risky decisions were also most likely best explained by a DDM incorporating ambiguity.Taken together, our results are evidence that ambiguity preferences across the time and probability domains are not independent but show some consistency despite the differing-attribute-wise versus integrated-value-decision strategies in each domain.