Abstract Estimating daily snow water equivalent (SWE) is critical for hydrological and climate applications, yet physical models often struggle to represent SWE, especially its interannual anomalies. In this study, we developed a hybrid physics‐guided machine learning (ML) model (hybrid model), by augmenting the Community Land Model 4.0 SWE simulations with a long short‐term memory (LSTM) network. The model is trained using the GlobSnow v3.0 data set and forced with meteorological data to estimate daily SWE at 0.5° over the Northern Hemisphere (NH). Our results demonstrate that the hybrid model significantly outperforms both the standalone physical and pure ML models in predicting SWE magnitude, timing, and anomalies, especially in complex mountainous regions. Explainable ML analyses suggest that the hybrid approach leverages the snow‐related physics while effectively utilizing observational data to enhance predictive accuracy. Moreover, we identify a widespread climate memory effect influencing SWE predictions across the NH, with memory‐dominant extreme events leading to greater SWE losses or gains relative to the average impacts of all extreme events, including those without strong memory effects. These findings underscore the hybrid model's ability to correct memory‐related biases that are not fully captured in current land surface models. Overall, our study highlights the value of hybrid modeling for improving SWE simulations and its potential as an alternative snow emulator within existing land surface models.