Comprehensive Performance Evaluation of Deep Learning Algorithms for Multi-Variable Climate Prediction: RNN, LSTM, and GRU Analysis

Abstract

The selection of appropriate deep learning architectures for climate prediction remains a  critical challenge in atmospheric sciences, with different algorithms showing varying performance across climate variables and geographical regions. This study presents a comprehensive comparative analysis of three prominent deep learning architectures Recurrent Neural Networks (RNN), Long   Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) for multi-variable climate prediction in the Middle East region. Using 42 years (1981-2022) of high-resolution MERRA-2 reanalysis data across nine Middle Eastern capitals, we evaluated the performance of these algorithms in predicting six key climate variables: maximum and minimum temperature, relative humidity, wind speed, solar radiation, and precipitation. Our analysis reveals significant performance differences across algorithms, with GRU demonstrating superior computational efficiency (14% faster training than LSTM) while  maintaining competitive accuracy. LSTM excelled in capturing long-term dependencies for temperature variables (R² > 0.95), while RNN showed adequate performance for simpler patterns but struggled with complex temporal relationships. The study provides detailed performance metrics, computational requirements, and practical guidelines for algorithm selection based on specific climate prediction tasks. Key findings indicate that algorithm choice should be tailored to the prediction target, with temperature  variables favoring LSTM, precipitation benefiting from GRU's efficiency, and humidity showing  comparable performance across all architectures. These results provide essential guidance for operational  weather forecasting systems and climate modeling applications, contributing to the optimization of deep learning approaches in atmospheric  sciences.