###### Abstract
Wavelet transforms have been extensively employed in meteorological research due to its excellent temporal and frequency domain localization characteristics [25; 26; 27; 28; 29]. [PERSON] et al. used several different wavelet transform methods to investigate the multiscale relationship between evaporation at two weather stations in northwestern Iran and five climate variables: average temperature, pressure, relative humidity, wind speed, and solar radiation. Their findings provide compelling evidence of a significant correlation between meteorological variables and evapotranspiration [30]. [PERSON] et al. analyzed the correlation between solar spectral irradiance, solar radiation, and magnetic flux in sunspot regions by using a cross and coherent wavelet transform. Their findings indicate that solar activity influences solar spectral irradiance; however, this effect is dependent on the wavelength of the solar irradiance [31]. The authors [PERSON] et al. employed a wavelet cross-spectrum and wavelet coherence analysis in order to propose a validation method for solar radiation models that deviate from time domain analysis. This approach enables the joint or separate validation of solar radiation models in both the phase domain and time domain [32]. [PERSON] et al. applied the wavelet transform theory in order to investigate temporal oscillation and the correlation among temperature, wind speed, and solar radiation in Taipei city. They discovered that there exists an inverse relationship between global solar radiation around Taipei City and wind speed, which exhibits a complementary pattern in terms of energy utilization [33]. Although several studies have involved the global solar radiation factor, there is a dearth of systematic and comprehensive analyses utilizing the wavelet transform method. The Morlet wavelet, being non-orthogonal, can be characterized as a complex sinusoidal function enveloped by a Gaussian centered at a specific frequency. By employing the Morlet complex wavelet as the mother wavelet, the original sequence undergoes a time-scale transformation resulting in complex wavelet coefficients. The oscillation periods of the time series at different time scales can be unveiled through an examination of both the modulus and the real parts of these coefficients [34]. By employing the Morlet wavelet as the mother wavelet, [PERSON] et al. conducted an analysis on the periodic oscillation of land subsidence and groundwater level time series in Beijing using a continuous wavelet transform. They discovered a spatial consistency between subsidence concentration in the northern part of Beijing Plain and the underground precipitation funnel. The prolonged reduction in groundwater extraction area within this subsidence region has resulted in a continuous decline of water levels, leading to irreversible and permanent water level depletion. Monitoring wells located outside the settlement zone exhibit evident changes in elastic deformation characteristics (seasonal patterns) during settling periods [35]. The temporal evolution characteristics of PM\({}_{2.5}\) concentration in the Yangtze River Delta Urban Agglomeration were analyzed by [PERSON] et al., utilizing a continuous wavelet transform with Morlet as the parent function. The findings indicate a step-like spatial decrease of PM\({}_{2.5}\) from northwest to southeast, accompanied by significant multi-scale time variation [36]. [PERSON] et al. employed the Morlet continuous wavelet transform in order to investigate the local intermittent oscillation of PM\({}_{2.5}\) in Chengdu. The findings reveal that the primary oscillation period exhibits temporal variability. Notably, there exist multiple oscillation periods within the ranges of 14-32 d, 62-104 d, 105-178 d, and 216-389 d, with a dominant period of 298 d observed throughout the entire evolution process.