Spatial-temporal modelling of convective storms with temperature-conditioned convective cell lifecycles

Stochastic convective storm generators are widely used for hydrological and climate-impact applications; however, most existing methods suffer from two fundamental limitations. First, once a convective cell is sampled, its properties are typically assumed to remain constant throughout its lifetime, neglecting the intrinsic evolution of cell intensity, size, and structure during growth and decay. Second, storm events are commonly generated by repeatedly sampling cell properties from fixed distributions, which limits inter-event variability and prevents systematic modulation of storm characteristics by large-scale weather or climate conditions, despite growing evidence that convective cell properties depend on variables such as near-surface temperature.

To address these limitations, this study develops a spatial–temporal convective storm generator that explicitly represents the lifecycle evolution of individual convective cells and its dependence on temperature. Storm arrivals are described using a point-process formulation, while individual storms are modelled as clusters of rainfall cells whose intensity and geometric properties evolve dynamically through time. The temporal evolution of cell properties is governed by a copula-based lifecycle model, within which key statistical parameters are conditioned on near-surface temperature using a regression-based model. Although the temperature dependence is introduced at the level of individual cell evolution, it propagates through the generator to influence storm-scale structure and inter-event variability.

The model is calibrated using 167 convective storm events observed over the Birmingham region (UK) between 2005 and 2017, identified and tracked with a state-of-the-art storm-tracking algorithm that provides detailed information on cell tracks and physical properties, including rainfall intensity, spatial extent, lifetime, storm duration, and motion. Results show that the proposed generator more realistically reproduces observed intra-event evolution, storm-to-storm variability, and extreme rainfall behaviour than conventional generators based on stationary cell assumptions. The resulting temperature-dependent storm generator offers a computationally efficient and physically consistent alternative to convection-permitting models for applications requiring large ensembles of convective rainfall realisations.