Assessing variations in meteorological parameters using global climate model (GCM) outputs and artificial neural networks

Abstract

The present research examines the impact of climate change on meteorological parameters using global climate models (GCMs) and artificial intelligence, with a case study in Fars Province, Iran. In this study, the meteorological parameters of minimum temperature, maximum temp-erature, precipitation, and solar radiation, as well as 12 GCMs from the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), are utilized. A statistical downscaling model (multilayer perceptron neural network) is employed to extract climate change predictions under two scenarios, representative concentration pathway (RCP)4.5 and RCP8.5, for four synoptic stations (Shiraz, Abade, Fasa, and Lar), each representing different climatic regions. Correlation analysis is used to identify the most influential predictor variables for each meteorological parameter. The results indicate a projected increase in maximum temperature by up to 2.67 °C, which could significantly impact agricultural productivity in Fars Province. This finding is accompanied by minimum temperature ranges from 0.23 to 2.71 °C, solar radiation increasing by up to 1.91 MJ/m², and precipitation fluctuation between a decrease of 7% and an increase of 36.5%. These findings suggest that the region may face increased agricultural stress due to higher temperatures and variable precipitation patterns, necessitating adaptive strategies for sustainable water resource management.