Deep Learning–based Climate Prediction in Iraq: A Comparative Analysis Aligned with IPCC Projection

Abstract

The Middle East and North Africa, especially Iraq, is going through some swift climate changes that pose serious threats to water supplies and agriculture, impacting many key sectors. This study looks at what the future temperatures in Iraq might be like until 2040 by using deep learning methods. We based our predictions on data collected from the Agricultural Meteorological Station in Baghdad from April 2011 to March 2025. The model did really well at predicting temperatures and solar radiation, though its accuracy for wind speed and rainfall was more hit-and-miss, which isn’t surprising given how unpredictable and fine-tuned those climates elements can be. The model evaluation showed a modest predictive power, with the deep learning model having the highest predictor coefficient (R² = 0.55) and the KNN model being the second highest (R² = 0.54). The SVR model had a slightly lower performance (R² = 0.51) and the MLP model had the lowest predictive precision (R² = 0.39). These results show that, while deep learning approaches provide a relatively better estimate of trends, the accuracy of the predictions remains low, highlighting the inherent complexity of regional climate prediction. The results clearly show temperatures are expected to keep rising, pointing to longer, more intense summers ahead. That could lead to higher energy use, less crop production, and worsening water issues. These findings line up with what the IPCC’s Sixth Assessment Report from 2021 said—that the Middle East and North Africa are likely to warm faster than the world on average, with more frequent and severe heatwaves. Overall, this study emphasizes the urgent need for effective planning and shows just how useful AI can be in helping Iraq figure out how to prepare for climate challenges.