  An ensemble post-processing method is developed to improve the probabilistic
forecasts of extreme precipitation events across the conterminous United States
(CONUS). The method combines a 3-D Vision Transformer (ViT) for bias correction
with a Latent Diffusion Model (LDM), a generative Artificial Intelligence (AI)
method, to post-process 6-hourly precipitation ensemble forecasts and produce
an enlarged generative ensemble that contains spatiotemporally consistent
precipitation trajectories. These trajectories are expected to improve the
characterization of extreme precipitation events and offer skillful multi-day
accumulated and 6-hourly precipitation guidance. The method is tested using the
Global Ensemble Forecast System (GEFS) precipitation forecasts out to day 6 and
is verified against the Climate-Calibrated Precipitation Analysis (CCPA) data.
Verification results indicate that the method generated skillful ensemble
members with improved Continuous Ranked Probabilistic Skill Scores (CRPSSs) and
Brier Skill Scores (BSSs) over the raw operational GEFS and a multivariate
statistical post-processing baseline. It showed skillful and reliable
probabilities for events at extreme precipitation thresholds. Explainability
studies were further conducted, which revealed the decision-making process of
the method and confirmed its effectiveness on ensemble member generation. This
work introduces a novel, generative-AI-based approach to address the limitation
of small numerical ensembles and the need for larger ensembles to identify
extreme precipitation events.
