Explainable Deep Learning for Multi-Step Meteorological Forecasting in Saudi Arabia: A Foundation for Air Quality Prediction

Accurate and interpretable forecasting of meteorological variables is essential for environmental monitoring and for the development of reliable decision-support systems. This study proposes an explainable multi-step deep learning framework for forecasting daily mean air temperature in central Saudi Arabia. The dataset comprises daily observations collected from 2020 to 2024, including air temperature, atmospheric pressure, relative humidity, and rainfall. Historical measurements from a 30-day lookback window were used to generate direct forecasts for 7-day and 30-day horizons. Four forecasting approaches were evaluated: Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), one-dimensional Convolutional Neural Network (CNN1D), and Transformer. Forecasting performance was assessed using Mean Absolute Error, Root Mean Square Error, and Mean Absolute Percentage Error, together with Taylor diagrams, residual distributions, and observed-versus-predicted analyses. The LSTM model achieved the highest predictive accuracy, obtaining MAE and RMSE values of 2.139 °C and 2.953 °C, respectively, for the 7-day horizon, and 2.345 °C and 3.134 °C for the 30-day horizon. To investigate model behavior, seven complementary explainable artificial intelligence methods were applied, including Integrated Gradients, Grad-CAM, SHAP, LIME, permutation importance, occlusion sensitivity, and saliency maps. These methods were selected to provide global feature-level, local prediction-level, and temporal explanations. The results show that recent air-temperature observations dominate short-term forecasts, whereas atmospheric pressure and relative humidity exhibit greater relative influence at the longer forecasting horizon. Rainfall contributes less consistently because of its sparse distribution within the study region. Overall, the proposed framework combines multi-horizon forecasting with comprehensive interpretability, providing a transparent approach for meteorological prediction and a methodological foundation for future environmental forecasting systems that integrate meteorological and pollutant observations.