<p>Drought is considered one of the most serious natural hazards affecting the world. The issue is particularly important with respect to agriculture, ecosystems, and water resources. The frequent occurrence of drought is an indicator of climate change and global warming. Therefore, there is a strong need to develop more accurate and reliable drought monitoring techniques. In this regard, traditional techniques used for drought monitoring are often less effective due to inconsistencies in precipitation data. In the present study, the we propose an innovative approach to improve regional precipitation estimates by incorporating auxiliary temperature information along with statistical process control techniques. Specifically, ratio and product estimators are employed to enhance the accuracy of precipitation time series using temperature data. In addition, an innovative weighting scheme is proposed to address issues related to outliers and seasonality. The improved precipitation data are then used to develop a novel drought monitoring index, termed the Adaptive Regional Drought Level (ARDL). The ARDL index incorporates an X-bar control chart to reduce anomalies and extreme variations in precipitation data. In this study, the ARDL method is applied to precipitation and temperature data from six meteorological stations in northern Pakistan to improve drought monitoring accuracy. The results demonstrate that the ARDL method effectively enhances drought monitoring compared to traditional techniques. This improvement is further validated by a reduction in the root mean square error (RMSE) of the precipitation estimates.</p>