Can a statistically downscaled stochastic rainfall model conditioned on climate variables sufficiently represent future rainfall scenarios?

Climate impact studies regarding hydrology require long precipitation time series of high spatial and temporal resolution. Global climate models (GCMs) provide global predictions of future climates, however they are a poor choice to accurately represent future surface precipitation conditions, especially at high resolutions. Instead, statistical downscaling from a GCM to a stochastic precipitation model is one common method to provide unbiased time series of arbitrary length for use within climate impact studies.

This study considers an alternating renewal stochastic rainfall model conditioned on fuzzy-rule based climate classes. The key research question for this study is whether stationarity of the climate classes can be assumed, meaning that changes to future rainfall can be explained by changes in climate class frequency alone. If stationarity of the climate classes cannot be assumed, what further steps, for example a delta-change approach, are required to adequately account for this non-stationarity.

An event based alternating renewal rainfall model has been conditioned on a fuzzy-rule based climate classification, using re-analysis climate data as input for the classification. The classification is created via an automated objective optimisation procedure that derives climate classes of non-mean (either dry or wet) rainfall behaviour.

The study area is the northern German federal state of Lower Saxony. ERA5 re-analysis climate data was used as input for the fuzzy-based classification. Previous studies using this classification method used atmospheric pressure data only, whereas this study also incorporates additional climate variables such as wind, temperature, humidity etc. 18 high-resolution rainfall gauges with a time series length of at least 15 years were used as observations for the rainfall model. A regional climate model (RCM) will be used as a reference for both past and future rainfall conditions in order to test the stated hypothesis. The climate classes derived from the re-analysis data will be reproduced for future climates using simulation results from a GCM.

Initial results indicate that the conditioning on climate classes using additional climate variables improves the single site performance of the rainfall model, particularly regarding extremes. The climate classes themselves were also shown to be more robust and diverse in terms of their rainfall behaviour when compared to classes generated from atmospheric pressure data alone. It is also hypothesised that the climate conditioned model will show improvements in predicting future precipitation conditions compared to previous studies.