In cold climates, the form of precipitation (snow or rain or a mixture of snow and rain) results in uncertainty in radar precipitation estimation. Estimation often proceeds without distinguishing the state of precipitation which is known to impact the radar reflectivity–precipitation relationship. In the present study, we investigate the use of air temperature within a nonparametric predictive framework to improve radar precipitation estimation for cold climates. Compared to radar reflectivity–gauge relationships, this approach uses gauge precipitation and air temperature observations to estimate radar precipitation. A nonparametric predictive model is constructed with radar precipitation rate and air temperature as predictor variables, and gauge precipitation as an observed response using a k-nearest neighbour (k-nn) regression estimator. The relative importance of the two predictors is ascertained using an information theory-based rationale. Four years (2011–2015) of hourly radar precipitation rate from the Norwegian national radar network over the Oslo region, hourly gauged precipitation from 68 gauges, and gridded observational air temperature were used to formulate the predictive model and hence make our investigation possible. Gauged precipitation data were corrected for wind induced catch error before using them as true observed response. The predictive model with air temperature as an added covariate reduces root mean squared error (RMSE) by up to 15 % compared to the model that uses radar precipitation rate as the sole predictor. More than 80 % of gauge locations in the study area showed improvement with the new method. Further, the associated impact of air temperature became insignificant at more than 85 % of gauge locations when the temperature was above 10 degrees Celsius, which indicates that the partial dependence of precipitation on air temperature is most important for colder climates alone.