EGU General Assembly 2020
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Rainfall trends in hindsight and in foresight

Theano Iliopoulou and Demetris Koutsoyiannis
Theano Iliopoulou and Demetris Koutsoyiannis
  • Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Greece (

Trends are customarily identified in rainfall data in the framework of explanatory modelling. Little insight however has been gained by this type of analysis with respect to their performance in foresight. In this work, we examine the out-of-sample predictive performance of linear trends through extensive investigation of 60 of the longest daily rainfall records available worldwide. We devise a systematic methodological framework in which linear trends are compared to simpler mean models, based on their performance in predicting climatic-scale (30-year) annual rainfall indices, i.e. maxima, totals, wet-day average and probability dry, from long-term daily records. Parallel experiments from synthetic timeseries are performed in order to provide theoretical insights to the results and the role of parsimony in predictive modelling is discussed. In line with the empirical findings, it is shown that, prediction-wise, simple is preferable to trendy.

How to cite: Iliopoulou, T. and Koutsoyiannis, D.: Rainfall trends in hindsight and in foresight, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8753,, 2020


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