Precise estimates of Snow Water Equivalent (SWE) are crucial for informed decision-making in regions like Northern Canada, where snow cover significantly contributes to springtime discharge. However, the sparse nature of the existing SWE monitoring network poses a challenge to comprehensively understanding the SWE distribution and variability. Reanalysis products like ERA5-Land provide long-term continuous SWE estimates, but our evaluation identified a negative bias (-61mm) in the estimated SWE and maximum underestimation was observed at high elevation (>1500m) areas. To correct these biases, we applied four correction methods: Mean Bias Subtraction (MBS), Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Random Forest (RF). RF exhibited the highest performance, reducing the Root Mean Square Error (RMSE) by 78\% and minimizing the annual mean bias from 61.2 mm to 0.01 mm. RF showed limited spatial transferability to regions characterized by very low or very high SWE values. However, it was able to capture the typical SWE values at most ecoregions. The bias-corrected SWE dataset will be used to validate and improve the estimation of snowmelt runoff, and to gain an improved understanding of the impact of SWE biases on snowmelt and subsequent spring soil moisture.