In this study, we use our analogue method and Convolutional Neural Networks (CNNs) to assess the potential predictability of extreme precipitation occurrence based on Large-Scale Meteorological Patterns (LSMPs) for the winter (DJF) of Pacific Coast California (PCCA) and the summer (JJA) of Midwestern United States (MWST). We evaluate the LSMPs constructed with a large set of variables at multiple atmospheric levels and quantify the prediction skill with a variety of complementary performance measures.

Our results suggest that LSMPs provide useful predictability of extreme precipitation occurrence at a daily scale and its interannual variability over both regions. The 14-year (2006-2019) independent forecast shows Gilbert Skill Scores (GSS) in PCCA range from 0.06 to 0.32 across 24 CNN schemes and from 0.16 to 0.26 across 4 analogue schemes, in contrast to those from 0.1 to 0.24 and from 0.1 to 0.14 in MWST.

Overall, CNN is shown to be more powerful in extracting the relevant features associated with extreme precipitation from the LSMPs than the analogue method, with several single-variate CNN schemes achieving more skillful prediction than the best multi-variate analogue scheme in PCCA and more than half of CNN schemes in MWST. Nevertheless, both methods highlight that Integrated Vapor Transport (IVT, or its zonal and meridional components) enables higher prediction skill than other atmospheric variables over both regions. Warm-season extreme precipitation in MWST presents a forecast challenge with overall lower prediction skill than in PCCA, attributed to the weak synoptic-scale forcing in summer.