Multimodel combining approaches can extract reliable climate information from a large number of climate projections by exploiting the strengths and discounting the weaknesses of each climate simulator; however, most of them (e.g., reliability ensemble averaging REA) assign weights to climate simulators without accounting for spatial and temporal variabilities in climate model skills. Here we tested several REAs and proposed a full version that reflects the spatiotemporal (ST) variability of climate model skills. Interperformance evaluations between REA versions showed that, on average, ST‐REA reduced the bias by 33.78% and the root mean square error by 11.61%. Therefore, spatial and temporal variabilities in climate model skills can enhance the overall reliability of precipitation projections. ST‐REA was applied to project future precipitations over South Korea by combining seven climate models: The spatially averaged projected changes were 2.77%, 8.15%, and 7.58% for the 2020–2039, 2040–2069, and 2070–2099 periods, respectively. Plain Language Summary The spatiotemporal variability of climate characteristics is one of the most pronounced climate behaviors. Thus, considering spatiotemporal variabilities during the assessment of climate models’ skills when performing a multimodel averaging may improve the overall reliability of the multimodel ensemble estimate. Nevertheless, most of the available multimodel averaging approaches (e.g., the REA) do not consider in parallel the spatial and temporal variabilities during climate model weighting. In this study, we presented an improved version of the REA, which can characterize the spatiotemporal variabilities at multiple sites and time steps during multimodel averaging. The proposed REA versions have improved weight assignation mechanisms (in respect to each climate simulator) and can handle multiple weather stations. Key Points Three augmented versions of reliability ensemble averaging were proposed to achieve reliable precipitation projections over South Korea The spatiotemporal variability of climate model skills within a multimodel approach improved the overall reliability of precipitation projections The spatiotemporal reliability ensemble averaging outperformed all versions of reliability ensemble averaging in precipitation projection