## 1 Introduction  
The atmospheric system exhibits characteristics and patterns of different time scales, ranging from hours to years. For instance, synoptic scale systems, which have typical 1-7-day cycles, are primary drivers of daily weather patterns [1; 2; 3; 4]. Seasonal variability, spanning 4-12 months, reflects the primary signals of seasonal changes and the annual cycle [5; 6; 7; 8]. The El Nino/Southern Oscillation (ENSO), with 2-7-year cycles, is one of the main drivers of global climate variability on an interannual time scale on an annual basis [9; 10; 11; 12]. In particular, the quasi-biweekly oscillation (QBWO, 10-20 days) and intraseasonal oscillation (ISO, 30-90 days) serve as the bridge linking low-frequency climate variability with synoptic weather systems. They are also critical for improving the accuracy of short- and medium-term climate predictions [13; 14; 15; 16]. When studying different atmospheric variabilities, statistical temporal filters are a common way to extract signals on certain time scales. For example, the running average is one of the simplest ways to smooth out high-frequency signals, such as interannual variability, in climate research [17; 18]. The Fast Fourier Transform (FFT) filter is another widely used method to extract periodic signals [19; 20]. In particular, for intraseasonal signal detection, Butterworth [21] and Lanczos [22] bandpass filters are often utilized to extract intraseasonal variability and have been applied to studies on boreal summer intraseasonal oscillation [23; 24], Madden-Julian Oscillation (MJO) [25; 26], QBWO [27; 28; 29], etc. Moreover, sharp bandpass filters were also utilized by [PERSON] et al. [30] to distinguish QBWO and ISO signals from the daily outgoing longwave radiation input. Among these statistical filters, the Lanczos filter is one of the most popular bandpass filters for identifying signals on quasi-biweekly and intraseasonal time scales. It has been reported that the Lanczos filter could reduce aliasing artifacts by suppressing high-frequency components, which are often evident in other filters such as the FFT filter, during filtering calculations. The Lanczos filter is also known to be able to reduce artificial oscillations caused by abrupt changes that may exist in the input series [31], which is often observed in the atmospheric system [32; 33]. On the other hand, the shortcomings of the Lanczos filter are also obvious. The Lanczos filter has edge effects when processing the beginning and end of the dataset [34]. Because the Lanczos filter relies on a certain number of neighboring data points to calculate the output value of each point, there may be loss of data at the very beginning and the end of the filtered output, depending on the length of the window applied. Due to the same reason, the filter may be unable to work properly if missing values exist in the input time series, and this will result in no valid output values near the missing values. Since incomplete time series are a common and unavoidable issue in meteorological data, especially for in situ observations, it remains a challenge to extract QBWO and ISO signals from station data by utilizing the Lanczos filter. These shortcomings of the Lanczos filter also exist in most statistical filtering methods. Aiming at addressing the above challenges of statistical temporal filters, this study introduces and employs a new type of filtering approach based on machine learning tools, or more specifically, a one-dimensional convolutional neural network (1D-CNN) [34; 35; 36]. Focus is particularly put on bandpass filtering due to its broad application in studies on QBWO and ISO, which serve as important atmospheric signals affecting weather variability worldwide [13], but the findings also hold for highpass and lowpass filters. In a recent study, [PERSON] and [PERSON] [34] proposed and showed that a CNN-based filter can be applied to time series with lengths comparable to the period of the signal being extracted.