Describing the spatio-temporal structure of precipitation extremes using wavelet transformation

The impact of a heavy precipitation event is determined not only by the total amount of precipitation but also by its spatial and temporal distribution. This study introduces a framework to quantify the key spatio-temporal properties of precipitation events - namely their characteristic time, length, and speed - using gridded datasets. 
To this end, we apply a spectral filtering approach based on wavelet decomposition. Wavelet decomposition has been proven to be highly effective in uncovering underlying frequency structures in time series and is well-suited for the analysis of two-dimensional spatial patterns. Previous applications to spatial precipitation fields (e.g., Buschow, 2024; Buschow & Friederichs, 2021) have demonstrated its potential to improve the understanding and description of precipitation events. We extend these methods to capture both spatial and temporal characteristics, providing for a comprehensive description of three-dimensional precipitation extremes.

Focusing on Germany, we analyze summer precipitation events using high-resolution datasets. These include the RadKlim dataset provided by the German Weather Service and a novel CPM ensemble, obtained from the NUKLEUS project. 
We assess the physical plausibility of the derived characteristics, examine their relationships to large-scale atmospheric dynamics, and also assess their changes with ongoing climate change. Our results reveal systematic patterns in the spatio-temporal organization of precipitation extremes. 
The framework presented here provides a robust tool for understanding extreme precipitation and offers potential for improved risk assessment and future climate studies. Our work is part of the BMFTR-funded ClimXtreme CoDEx project.