Characterizing variability and changes in precipitation, including extreme precipitation, is important for understanding and monitoring natural hazards. Past studies have often used frequency-based approach to quantify changes in the events, which requires us to define an anomalous event by calculating a frequency of threshold exceedance and aggregating across space. However, the choice of the threshold value, time window, and spatial boundary for defining anomalies is not trivial. Therefore, a method that allows a characterization of precipitation without any prior specification of anomaly criteria (such as regional boundaries or fixed temporal windows) is beneficial. This study uses functional principal component analysis (FPCA) to characterize seasonal mean and extreme precipitation using measurements from the Global Historical Climatology Network Daily over the contiguous United States. FPCA is a flexible method that allows us to identify modes of temporal variability and spatial patterns of precipitation variability at a variety of scales. Using this method, we also characterize nonlinear trends in the distribution of precipitation and detect anomalous spatio-temporal events.