The Advanced Meteorology Explorer: a novel stochastic, gridded daily rainfall generator

•A novel stochastic daily rainfall modelling framework, the Advanced Meteorology Explorer, is developed based on hidden Markov models and copulas.•This framework allows for the simulation of physically consistent synthetic daily rainfall data, coherently in space and time, on a high-resolution grid.•Simulations are shown to accurately capture rainfall occurrence and intensity, as well as long-duration drought behaviour.•These simulations can be effectively used for drought and flood risk assessment in any industry impacted by rainfall. Synthetic rainfall simulations from stochastic models are commonly used for water resource management, as they are able to provide a wider range of meteorological conditions than those seen in the observed record. Here, we present a novel stochastic rainfall modelling framework, the Advanced Meteorology Explorer (AME), which combines and extends existing methods to enhance model flexibility, and meet a number of key water industry needs. This framework allows for the simulation of physically consistent synthetic daily rainfall data, coherently in space and time, on a high-resolution grid over a region of interest. The AME uses an advanced hidden Markov model structure within a Bayesian hierarchical framework to represent daily rainfall at a set of locations in a region, conditional on important climate drivers. The climate drivers included in the rainfall model at each location are able to vary using penalised regression, ensuring a transferable model that can be applied to different locations without adaptation. The dependence between locations is modelled following a flexible copula approach, able to capture varying dependence structures within the data, allowing for spatially coherent simulations at the modelled locations. Simulations are then interpolated to a high-resolution grid using a terrain adjusted inverse-distance weighted interpolation method. The AME framework is applied to 105 years (1914–2018) of daily rainfall data at 39 sites in the Greater Anglian region of the UK, and used to generate 1000 alternative realisations of the same period on a 5 km grid over the region. Validation of these simulations shows how the AME framework is able to accurately capture rainfall occurrence and intensity, as well as long-duration meteorological drought behaviour, important for quantifying water resource risk in this dry region. This framework has the potential to be applied to other regions, incorporate additional weather v