High‐frequency time‐series data from HVAC systems offer detailed insights into dynamic performance but incur significant storage and processing burdens. This paper presents an intelligent, deep‐learning-based downsampling framework that simultaneously compresses data and preserves critical temporal features. Raw one‐second measurements of chiller supply temperature and instantaneous flow rate are aggregated into multi‐resolution feature sets (1 min, 30 s, 20 s, and 10 s) using statistical descriptors, and a long short‐term memory (LSTM) network is trained to reconstruct the original one‐second flow profile from these low-dimensional summaries. Comprehensive evaluation, including storage efficiency and mean squared error (MSE) against ground truth, demonstrates that the 20-s aggregation achieves the best trade-off, reducing average reconstruction error (MAE = 19.8 L/min, MAPE = 2.40%) and peak deviation (146.4 L/min) while cutting storage requirements by an order of magnitude. The model also reduces the MSE during rapid ramp-up peaks by approximately 12% relative to one-minute baselines and maintains high fidelity in steady-state regimes. These results confirm that LSTM‐based downsampling can dramatically lower data volumes with minimal loss of high-frequency information, enabling more efficient monitoring and fault detection in HVAC applications.