Abstract
Climate change is one of the most serious modern problems affecting the Earth's atmosphere, as it causes a range of harmful effects worldwide. Due to the uneven nature of climate data, forecasting climate change is a challenging task today. Many previous studies in climate and machine learning have used recurrent neural networks (RNNs) and long short-term memory (LSTM) models to predict climate trends. Although these models are effective at identifying long-term trends in data, they often fail to satisfy physical laws such as energy conservation, mass balance, and thermodynamic principles. In this research, the aim was to develop physics-informed RNN and physics-informed LSTM models and compare their effectiveness with standard RNN and LSTM models. The study utilized data on air temperature at 2 meters above the surface for the cities of Astana, Almaty, and Shymkent for model training, validation, and testing. According to the results, physics-informed models achieved the lowest root mean square errors in Almaty (3.52 °C) and Shymkent (3.80 °C). RNN and LSTM models performed better in Astana (RMSE = 5.44 and 5.47 °C), where seasonal changes are relatively abrupt. These findings demonstrate that PINNs give more physically consistent forecasts for moderate climates, while conventional recurrent models remain more effective for locales with highly variable conditions.