Drought identification is crucial to water resources management and planning. Different drought indices have been developed and their complexity and applicability vary. The objectives of this research are to develop a new integrated drought index with the capability of identification of drought and to further customize drought categorization for cold climate regions. Specifically, a new hydroclimatic aggregate drought index (HADI) is developed by coupling with a gridbased hydrologic model and applying the R-mode correlation-based principal component analysis. The HADI is a composite drought index, which assesses the anomalies of rainfall, surface runoff, snowmelt, and soil moisture in the root zone. Furthermore, joint probability distribution function of drought frequencies and classes as well as conditional expectation are used for drought categorization. The HADI was applied to the Red River of the North Basin (RRB) and its performance was evaluated by comparing with the Palmer Drought Severity Index (PDSI) and the U.S. Drought Monitor (USDM) products. Based on the impacts of drought on agriculture, the HADI outperformed the PDSI in identification of droughts in the RRB. Although the HADI and USDM showed a good agreement in identification of drought periods, the drought area coverages for each drought category from the two methods differed. The new customized drought categorization based on variable threshold levels accounted for the variations in both time and geographical locations. The new HADI, together with the customized drought categorization, is able to provide more accurate drought identification and characterization, especially for cold climate regions.