Abstract Climate warming has increased the risk of recurrent drought hazards. Previous research has indicated potential associations between climate warming and extreme climate events. Due to short distance spatial variability in meteorological variables, the prevalence of extreme values may result in biased inferences or reduce the scope of regional drought analysis. Therefore, precise inferences of spatiotemporal characteristics of regional drought are challenging. In this study, we provide a new weighting scheme to diminish the effect of extreme values for the regional aggregation of precipitation data. We used K – Components Gaussian Mixture Models (K–CGMM) based on the standardization procedure to achieve maximum computational accuracy. Consequently, the article proposes a new regional drought index – the Bias Diminished Weighted Regional Drought Index (BDWRDI). We assessed the performance of BDWRDI by calculating the Standardized Precipitation Index (SPI) using data from six meteorological stations in the Northern area of Pakistan. We found that the proposed weighting scheme can greatly diminish the effect of extreme values during the spatiotemporal aggregation of precipitation data. In addition, the strong positive correlation of BDWRDI with individual SPIs endorses the proposed weighting scheme and the proposed drought indicator for regional drought analysis.
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