Rainfall is the main input to most hydrological systems. To assess flood risk for a catchment
area, hydrologists use models that require long series of subdaily, perhaps even subhourly …

Numerous studies across multiple disciplines search for insights on the effects of climate
change at local spatial scales and at fine time resolutions. This study presents an overall …

High‐resolution space‐time stochastic models for precipitation are crucial for hydrological
applications related to flood risk and water resources management. In this study, we present …

Many multi-site stochastic models have been proposed for the generation of daily
precipitation, but they generally focus on the reproduction of low to high precipitation …

This work describes a simple and parsimonious multisite Markov model deduced from
bivariate copula‐based mixed distributions useful for modeling and generating daily rainfall …

Continuous rainfall sequences are often used as inputs in hydrologic modeling, particularly
where a probabilistic assessment is required. Continuous rainfall sequences provide a …

Subdaily rainfall data, though essential for applications in many fields, is not as readily
available as daily rainfall data. In this work, regression approaches that use atmospheric …

Areal reduction factors (ARFs) transform an estimate of extreme rainfall at a point to an
estimate of extreme rainfall over a spatial domain, and are commonly used in flood risk …

Effective implementation of a scenario-neutral climate impact assessment relies on the
integration of many modelling components at multiple stages: from the generation of …

This paper provides a historical review and critique of stochastic generating models for
hydrological observables, from early generation of monthly discharge series, through flood …