A duration-augmented binary Markov chain for rainfall occurrence with long dry spells

TL;DR

This paper introduces a duration-augmented binary Markov chain model for rainfall occurrence that better captures long dry spells by linking to renewal chains and using flexible distribution specifications.

Contribution

It develops a novel duration-augmented Markov chain framework with flexible tail modeling, improving rainfall persistence simulation over standard models.

Findings

Model accurately reproduces long dry spells in diverse climates.

Outperforms standard Markov models in persistence and high-quantile extrapolation.

Flexible framework extends to multi-state environmental applications.

Abstract

Simulating realistic wet and dry spells is central in weather generators and climate-impact studies. While finite-order Markov chains are standard, they often fail to reproduce persistent dry conditions due to their inherent subexponential decay. We model rainfall occurrence by introducing a duration-augmented binary Markov chain. We establish a link with alternating renewal chains, enabling flexible parametric modelling of wet and dry spell duration distribution. We model those using two regime-adapted specifications from the general class of extended Generalized Pareto Distributions, yielding flexible tail behaviour across various climates. We use estimation methods adapted to each specification. Our model is applied to around 200 stations in the South of Europe spanning diverse Mediterranean and continental climates. We compare this framework to standard Markov models in characterising persistence and high-quantile extrapolation. The approach is generic, extending naturally to multi-state settings or other binary sequence applications in environmental statistics.