Probabilistic Rainfall Downscaling: Joint Generalized Neural Models with Censored Spatial Gaussian Copula

This research presents a method for converting large-scale weather predictions into detailed local rainfall forecasts using neural networks and statistical models. The approach works in two steps: first, it uses joint generalized neural models (neural networks that predict the parameters of probability distributions) to estimate rainfall distributions based on coarse weather data, and second, it uses a censored latent Gaussian copula (a mathematical model that captures how rainfall patterns are related across nearby locations) to ensure spatial coherence. The method was tested on UK weather data and performed better than existing techniques.