Non-stationary extended generalized Pareto distribution for joint assessment of trends in the bulk and extreme precipitation

Accurately quantifying the impact of climate change on past and future precipitation, especially in terms of extreme precipitation events, remains a significant challenge. Furthermore, the scarcity and high variability of extreme events make this task particularly daunting. Current approaches based on extreme value theory (EVT),  relying on annual maxima series or exceedances above large thresholds, are limited in their efficiency as they only consider a small fraction of the available data. Additionally, these methods do not model the bulk of the distribution, which has applications in areas such as water resources management, urban water supplies, and hydropower. To address these limitations, Naveau et al. (2016) proposed the Extended Generalized Pareto distribution (EGPD), which models the entire non-zero precipitation range while remaining consistent with EVT in both lower and upper tails. While the EGPD has seen wide applications in modeling precipitation in various regions, its application has predominantly been within a stationary framework (e.g. Haruna et al.,2022, 2023). This study explores the potential of a non-stationary version of the EGPD to jointly model trends in both the bulk of the precipitation distribution and in the extremes. The non-stationarity is accommodated by allowing the EGPD parameters to be parametric functions of relevant explanatory variables. The proposed model is then applied to precipitation datasets in Switzerland, a region where long term warming of twice the global average has already been experienced.

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