Rainfall forecasting: a review of current practice

Daily rainfall forecasting is crucial for the effective management of water resources. Over the past few decades, forecasting techniques have evolved significantly to enhance prediction accuracy. This paper reviews the development of daily rainfall forecasting methodologies, highlights recent advancements, and outlines key challenges and future research directions in the context of a changing climate, data limitations, and the growing demand for hyperlocal predictions. Early methods primarily relied on empirical and basic statistical approaches, such as autoregressive models and Markov chains, which provided limited spatial and temporal resolution. With advancements in technology and increased data availability, numerical weather prediction (NWP) models became widely adopted. More recently, the integration of machine learning (ML) and deep learning (DL) techniques has brought a transformative shift to the field. In many studies, artificial intelligence (AI)-based approaches such as artificial neural networks (ANNs), long short-term memory (LSTM) networks, and hybrid ensemble models, have demonstrated superior capabilities in capturing complex, nonlinear patterns within large-scale meteorological datasets. Furthermore, the use of satellite data, reanalysis products, and high-resolution climate models has been shown to enhance forecasting reliability. It is anticipated that future rainfall forecasting will rely heavily on adaptive, data-driven systems, integration with climate change projections, and the application of interpretable AI methodologies.