Comparative Analysis of Statistical and Deep Learning Models for Daily Climate Forecasting: A Case Study on the Delhi Dataset

As the impacts of climate change continue to intensify, accurate forecasting of meteorological parameters is of critical importance for many applications ranging from agricultural planning to disaster management. This study presents a systematic comparison of seven forecasting models – two baseline statistical (ARIMA, SARIMA) and five deep learning (LSTM, CNN, MLP, CNN-BiLSTM-Attention, and Wavelet-CNN-LSTM) – for daily prediction of mean temperature, humidity, wind speed, and mean pressure using Delhi climate data covering 2013–2017. Hyperparameters for all models are optimized via Random Search, and 10 independent runs with different random seeds are conducted to ensure statistical reliability. Model performance is evaluated using RMSE, MAE, MAPE, MASE, and R² metrics, while the Friedman test with Nemenyi post-hoc analysis is employed to assess statistical significance of performance differences. Results indicate that the Wavelet-CNN-LSTM model achieves the best overall ranking, followed by LSTM, MLP, and CNN, all of which performed significantly better than the ARIMA and SARIMA models. High prediction accuracy is obtained for temperature (R² = 0.922) and pressure (R² = 0.9), whereas wind speed remains the most challenging variable for all models (R² < 0.2) due to its inherently stochastic nature. Predictability analysis confirms that the chaotic behavior of wind speed, characterized by low autocorrelation and high variability, imposes fundamental limits on deterministic forecasting at daily resolution.