Cluster-based downscaling of precipitation using Kolmogorov-Arnold Neural Networks and CMIP6 models: Insights from Oman

Accurate precipitation predictions are crucial for addressing climate change impacts on water resources, especially in arid regions like Oman. Therefore, this study evaluates three machine learning models—Random Forest (RF), Multilayer Perceptron Neural Networks (MLP-ANN), and Kolmogorov-Arnold Neural Networks (KANNs)—to downscale and predict precipitation patterns under climate scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5. We assessed each model's ability to reproduce past trends and predict future precipitation using historical data from 1995 to 2014 and projections from 2020 to 2099. The KANN model demonstrated exceptional proficiency in forecasting extreme precipitation occurrences, especially in the most severe scenario (SSP5-8.5). The MLP-ANN model offered a balanced methodology, yielding dependable forecasts that are adaptive to fluctuating situations, even amongst small increases in precipitation and uncertainty. The RF model generated the most reliable forecasts, suggesting small increases in future precipitation while closely correlating with historical data. The study indicates distinct seasonal patterns, with peak precipitation occurring during the monsoon season from June to August. The RF model predicted more intense and uniformly distributed precipitation during this period, demonstrating its advanced data processing capabilities. The geographical patterns predicted by each model exhibited temporal stability, highlighting their consistent reliability, which is essential for precise climate predictions. This comparative research highlights the significance of choosing a suitable machine learning model according to distinct forecasting requirements. Effective downscaling methodologies are essential for informed water resources management, particularly in areas susceptible to cyclones and water shortages. These results provide essential direction for policymakers to improve climate resilience, optimize water infrastructure, and formulate adaptation measures in Oman and other dry locations.