Journal of E-Technology Nguyen Minh Tuan 17 3 2026 https://doi.org/10.6025/jet/2026/17/3/103-121 https://www.dline.info/jet/fulltext/v17n3/jetv17n3_1.pdf This study investigates the multiscale temporal dynamics of near surface temperature variability in Ho Chi Minh City, Vietnam, utilizing a comprehensive three-year hourly meteorological dataset comprising 28,508 observations. A unified analytical framework integrating time domain, frequency domain, and time frequency techniques was applied to robustly isolate periodicities and assess structural stability. Methodologies included Fast Fourier Transform (FFT), Fisher's g-test, Monte Carlo surrogate modeling, stationarity diagnostics (ADF and KPSS tests), autocorrelation analysis, classical seasonal decomposition, and Continuous Wavelet Transform (CWT) coupled with multi resolution decomposition. Results reveal a highly significant, deterministic 24-hour diurnal cycle governing temperature, relative humidity, and precipitation, primarily driven by intense solar forcing and regional land sea breeze regimes. Conversely, mean sea level pressure is dominated by low frequency seasonal fluctuations of approximately 1.08 years. Stationarity tests confirm the dataset's inherent non stationarity, while autocorrelation diagnostics reveal strong temporal persistence and long memory. Furthermore, wavelet scalograms and multi-resolution decomposition illustrate that while long term climatic forcing accounts for the overwhelming majority of statistical variance, short term weather anomalies introduce critical high frequency fluctuations. These findings underscore the limitations of standalone linear models for environmental forecasting. The identified hierarchical temporal structure strongly supports the development of hybrid predictive architectures. Future forecasting systems should integrate wavelet based multi resolution feature extraction, routing long term trends through deep learning frameworks while optimizing short term residuals via machine learning ensembles to significantly enhance urban climate and energy demand predictions.