Droughts are multifaceted climate phenomena influenced by atmospheric, oceanic, and terrestrial processes operating across varying spatial and temporal scales. These interacting triggers make drought events challenging to predict and manage, particularly in regions with various climatic regimes. Traditional drought assessments primarily emphasize precipitation and temperature, often overlooking the critical role of relative humidity (RH), a key driver of atmospheric moisture and evapotranspiration dynamics. Recognizing this gap, the present study introduces a new Multivariate Standardized Relative Humidity Index (MSRHI) to improve regional drought assessment in Pakistan. Here, "multivariate" reflects the integration of multiple RH time series from stations within each climatic zone, rather than the inclusion of different climatic variables. The proposed approach involves the statistical standardization of the RH time series through the fitting of 32 univariate probability distributions in each climatic zone. The optimal distribution is selected using the Bayesian Information Criterion (BIC), ensuring an accurate probabilistic representation of regional humidity variability. Principal Component Analysis (PCA) is then used within five defined climatic zones, coastal, arid, semi-arid, humid, and semi-humid, to extract dominant spatial features. PCA reduces local noise by emphasizing shared spatial patterns and down-weighting isolated anomalies. The first principal component (PC1), which captures the largest variance and dominant regional drought signal, is standardized to construct the MSRHI. Empirical results demonstrate that the MSRHI offers a more stable, consistent and spatially coherent representation of drought conditions than individual station-based indices. The PCA findings reveal strong spatial correlations in RH patterns, especially in the humid and coastal zones, underscoring the robustness of the multivariate approach. From a policy perspective, the MSRHI provides a robust and regionally adaptive scientific tool for early warning of drought, water resource planning, and climate risk management. Its ability to integrate spatial variability and improve drought representation makes it particularly valuable to policy makers, disaster management authorities, and climate-sensitive sectors in Pakistan and similar regions.