Extreme precipitation is expected to increase in a warming climate. Station-based measurements of precipitation and dew point temperature are often used to estimate observed precipitation-temperature scaling rates. In this study, we use three different approaches which rely on either raw or normalized data to estimate scaling rates and produce predictions of extreme precipitation. We find that normalizing the data first can improve our estimates of precipitation-temperature scaling rates.