Accurate short-range rainfall forecasts, known as nowcasts, are crucial for providing early warnings of extreme precipitation and flooding, especially in urban areas with high population density. Traditional nowcasting techniques, such as extrapolating radar echoes from constant altitude plan position indicator (CAPPI) or lowest-angle plan position indicator (PPI), often struggle to predict the development and dissipation of convective storms effectively. To address these limitations, methods like RadVIL, which uses mass balance equations of Vertically Integrated Liquid (VIL), and Spectral Prognosis (SPROG), which employs an autoregressive (AR) model, have been developed. Recent advancements include Autoregressive Nowcasting using VIL (ANVIL), which models the growth and decay of VIL using an autoregressive integrated (ARI) process, decomposing VIL into multiple spatial scales and applying a separate ARI model to each scale. Another advancement called SPROG-Localized (SPROG-LOC) extends the SPROG approach, which is the deterministic version of Short-Term Ensemble Prediction System (STEPS), by estimating spatially localized parameters of the AR process, improving the accuracy of rainfall forecasts. Building on these recent methods, we propose a novel approach that combines ANVIL and SPROG-LOC, termed SLANVIL (SPROG-Localized Autoregressive Nowcasting using VIL). This integrated method leverages the strengths of both techniques, aiming to improve nowcasting performance, particularly in scenarios with large, non-uniformly distributed precipitation areas and isolated convective features. While we are currently in the process of obtaining preliminary results and its probabilistic extension, we will present a first validation of our method for lead times up to 2 hours, comparing its forecast skill at various precipitation thresholds and spatial scales to established and operational nowcasting methods, such as pySTEPS-BE.