A novel statistically-based approach to regionalize extreme precipitation events using temperature data

Extreme precipitation events have been increasing and intensifying over the past few decades, posing challenges for modeling and prediction, as well as for policy and decision making. While traditional approaches often focus solely on studying the precipitation process, recent studies advocate for considering multiple processes and variables to better understand the drivers and anomalies of precipitation. This is especially underexplored in South America. To address this, we propose a novel approach that combines time series modeling and quantile regression to estimate the extreme quantiles of precipitation based on maximum daily temperatures. This methodology helps in understanding the relationships between these processes and contributes to identifying gauge stations with coherent climatic covariability, offering valuable insights into the regionalization of extreme events.