In recent decades, global climate change has triggered a rise in extreme environmental phenomena, including prolonged droughts, intensified precipitation events, and shifts in tidal patterns. This study focuses on the application of the observations from Global Navigation Satellite System (GNSS) signals for monitoring and classifying climatic conditions, with particular emphasis on drought. Using daily vertical displacement data from a GNSS station in California (2005–2023), we developed a robust analysis framework. It includes data cleaning (removing outliers, filling gaps, detecting offsets, and modeling noise), trend and seasonal pattern extraction through Singular Spectrum Analysis (SSA), feature generation (like amplitude, energy, and dominant frequency), labeling based on the Standardized Precipitation-Evapotranspiration Index (SPEI), and classification using a hybrid CNN-Transformer model. The results demonstrate the model’s capability to accurately detect drought periods (SPEI < -1) characterized by diminished amplitudes in seasonal components and heightened noisy fluctuations, as well as wet periods (SPEI > 1) marked by elevated energy in semi-annual signals. The model was evaluated with an overall accuracy of 83.3 percent, an F1-score of 0.90 for the drought class, and successful classification of newly observed data (2024–2025) and scenario-based extrapolation for 2026–2029. This approach, independent of traditional meteorological data, underscores the potential of GNSS as a geodetic tool for environmental monitoring, albeit with limitations such as reliance on single stations and the need for supplementary datasets. The methodology holds promise for enhancing early warning systems and climate models. At the time of model development (2023), the period 2024–2029 was treated as future data and used to assess the extrapolation capability of the proposed model. While real observations for 2024–2025 later became available, the remaining years represent a scenario-based forecast derived from learned temporal patterns rather than operational meteorological prediction.