Abstract: Global Navigation Satellite System (GNSS) has become a routine technical tool for continental crustal deformation monitoring, geodynamic research, and disaster early warning. In large-scale GNSS networks, the spatial correlation of common mode errors (CME) between stations decays rapidly with increasing distance, making it difficult for spatiotemporal filtering methods such as principal component analysis (PCA) and independent component analysis (ICA) to extract components that exhibit consistent spatial responses across all stations. To address this issue, this study divides the study area into six subregions-Chuandian region, Northeast region, North China region, South China region, the Qinghai–Xizang Plateau region, and Xiyu region—based on the first-level active tectonic blocks in mainland China, and applies the PCA method to perform three-dimensional GNSS coordinate time series spatiotemporal filtering for each subregion. The experiment selects 208 GNSS coordinate time series with qualified data quality from the Crustal Movement Observation Network of China (CMONOC) spanning from 2013 to 2023. Data preprocessing includes trend removal, step discontinuity correction, and gross error elimination. The filtering performance is quantified using root mean square (RMS), power-law noise amplitude, and empirical cumulative distribution function (ECDF), while the sources of CME are analyzed in combination with atmospheric loading, hydrological loading, and non-tidal oceanic loading corrections. In the noise characteristic analysis, a combined white noise plus power-law noise model is adopted to describe the stochastic properties of the coordinate time series, and the maximum likelihood estimation method is used to estimate noise parameter variations before and after filtering. In the geophysical interpretation of CME, harmonic analysis is introduced to extract annual and semi-annual periodic signals, and a mismatch index (D-value) is employed to quantitatively evaluate the consistency of periodic components between CME and environmental loading displacements. The results demonstrate that the sub-regional PCA filtering reduces the average RMS of the north (N), east (E), and vertical (U) components by 21.39%, 20.20%, and 29.15%, respectively, significantly outperforming global PCA. The average power-law noise amplitudes are reduced by 31.07%, 31.13%, and 33.41%, respectively, indicating more effective suppression of noise in the time series. After environmental loading corrections, the vertical CME extracted by partitioned filtering exhibits higher periodic consistency with surface mass loading displacements, confirming that the vertical CME mainly originates from the combined effects of atmospheric, hydrological, and non-tidal oceanic loadings. The findings provide a new approach for refined CME processing in large-scale GNSS network coordinate time series and high-precision monitoring of regional deformation.