A statistically sufficient number of rainfall estimates is necessary to model precipitation-induced landslide hazards accurately. This is particularly important where existing gauges are scanty and widespread, such as in Kerala State of southern India, an area characterized by heavy monsoon rains and thus inherently prone to massive landslides. To identify potential new gauge sites, we evaluated general slope stability, landslide density, and land cover. We optimized locations for installing new rain gauges through a sequential process based on interpolation errors; this was validated by comparing the resulting statistics with a random selection. Based on these procedures, we installed eight new rain gauges. We also used a satellite-gauge algorithm (conditional merging) to assess the effect of rain gauge network expansion for precipitation measurements. The Pearson correlation coefficient indicated statistically significant measurement improvement after the new rain gauge installation. We applied the leave-one-out cross-validation (LOOCV) test to improve rain gauge modeling. We divided the study area into rain gauge influence sections and calculated the root mean square error (RMSE) for each. We found that the optimized expanded rain gauge network locally produced a 20–25 percent reduction of RMSE compared with the original gauge distribution.