Probabilistic Monthly Precipitation Forecasting over Morocco Using xLSTM and Large-Scale Climate Predictors

Accurate forecasting of precipitation remains a central challenge in climate science, primarily due to the strong temporal and spatial variability of rainfall, a difficulty that is further intensified by the ongoing impacts of climate change. Recent developments in machine learning have facilitated the design of more accurate and robust predictive frameworks. In this context, the present study implements and evaluates three deep learning architectures; Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Extended Long Short-Term Memory (xLSTM); to forecast monthly precipitation at 27 meteorological stations distributed across Morocco, for lead times ranging from 1 to 4 months. The models are trained using a heterogeneous set of large-scale climatic predictors, including sea surface temperature (SST) over the Atlantic Ocean and the Mediterranean Sea, the East Atlantic pattern (EA), the Madden–Julian Oscillation (MJO), the El Niño–Southern Oscillation (ENSO), the Mediterranean Oscillation (MO), the North Atlantic Oscillation (NAO), and the Western Mediterranean Oscillation (WeMO). To identify the most influential predictors at each station, a principal component analysis (PCA)-based feature selection procedure is implemented. The results indicate that precipitation variability across the study area is predominantly controlled by the MO, NAO, and WeMO indices. Probabilistic forecasts are then generated using Monte Carlo dropout, enabling the networks to approximate Bayesian inference and thereby quantify predictive uncertainty and associated confidence intervals. Relative to conventional LSTM and GRU configurations, the xLSTM architecture exhibits superior predictive performance across all stations and lead times, with notably reduced uncertainty, particularly in the representation of extreme precipitation events. Overall, the models demonstrate robust skill in northern Morocco, with coefficients of determination (R²) ranging from 0.82 to 0.96 for a 1‑month lead time. However, predictive skill degrades toward the southern region, characterized by arid to semi-arid climatic conditions, where R² values decrease to 0.36–0.86. These results indicate that xLSTM effectively captures long-range temporal dependencies and low-frequency, high-intensity rainfall events, thereby representing a promising framework for improving probabilistic monthly precipitation forecasts in climatically heterogeneous regions such as Morocco.