<p>Missing data arises from collection instabilities, observer shortages, and equipment malfunctions, undermining the integrity of atmospheric and climatic analyses in local datasets. Regions such as the Global South often encounter significant challenges in collecting historical climate data due to insufficient equipment and constrained spatial coverage, posing additional obstacles to conducting comprehensive climate studies. This study aims to assess the performance of imputation climatological data methods for daily rainfall data within a mountainous tropical region in the Global South. This study focuses on the municipality of Petrópolis, located in Rio de Janeiro, Brazil, which is equipped with five rain gauges providing data from 1940 to 2022 and exhibiting minimal data gaps. Using widely recognized methodologies from scholarly literature, including Simple Arithmetic Mean (SAM), the Normal Ratio Method (NRM), the Best Estimate Method (BE), and the Inverse Distance Method (ID), this study assesses imputation accuracy using metrics such as Mean Absolute Error (MAE), Pearson’s Correlation (r), and Root Mean Square Error (RMSE), with MAE serving as the primary performance indicator. It is noticeable that only linear techniques were used for precipitation gap filling in this study. The results show the smallest deviations at lower-altitude stations in the leeward portion for the BE, SMA, and NRM methods, likely due to the placement of the rain gauges. Conversely, the gauges at higher altitudes exhibited the largest deviations compared to those at lower altitudes, although the deviation results remained within acceptable limits. Thus, the two highest-altitude gauges were filled using the SMA and NRM methods, while the remaining gauges were filled using NRM, SMA, and NRM, respectively.</p>