Evaluation of fractal theory and LARS-WG downscaling model in precipitation and temperature forecasting via a climate change approach

ABSTRACT In recent years, the disruption of the global climate balance due to the increase in greenhouse gases has introduced complexity in predicting hydrological processes. This research presents the changes in precipitation and temperature for the next 30 years (2024–2054) compared with the base period of 2002–2020, utilizing the HadCM3 and CanESM5 downscaling models from the CMIP6 models under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Additionally, it examines the application of fractal theory to the Silakhor Plain. The results obtained from the climate models indicate that the prediction accuracies of the HadCM3 and CanESM5 models under the mentioned scenarios are very close to each other. In the best-case scenario, the optimistic scenario (SSP1-2.6) of the HadCM3 model, the average root mean square error values for the climatic parameters of precipitation and minimum and maximum temperatures are 0.39 mm, 1.26 °C, and 1.48 °C, respectively. The fractal model for the precipitation parameter falls within the range of strong stability (0.65–0.75). Similarly, the fractal model for the minimum and maximum temperatures is located in a very strong stability range (0.80–1.00), indicating that the time series has a long-term memory and an increasing trend.