Improving multisite precipitation generators based on generalised linear models

Precipitation generators are statistical models used to produce synthetic sequences of (multisite) precipitation for hydrological applications such as flood risk assessment and water resource management. Among these, approaches based on generalized linear models (GLMs) are widely used and often perform competitively with state-of-the-art alternatives, but they face limitations in representing seasonal variation in extremes and in flexibly capturing covariate effects on the precipitation distribution. In this paper, we extend the GLM framework in two directions. First, we introduce generalised additive models for location, scale and shape (GAMLSS) for precipitation generation. These models allow the use of spline-based model terms to flexibly capture covariate effects and allow covariates to influence multiple distributional parameters, thereby increasing flexibility in representing variation in both the mean and variance of the precipitation distribution. Second, we adapt a transformed Gaussian fields approach to jointly account for spatial dependence in both precipitation occurrence and intensity, thus allowing for potential cross-dependence between the two. A further contribution is to investigate the sensitivity of model performance to data resolution, highlighting that rounding in data pre-processing can substantially affect the reproduction of extremes. Using a well-studied daily precipitation dataset, we demonstrate that these extensions improve the realism of simulated sequences, particularly with respect to extremes, and capture spatial dependence well in both occurrence and intensity.