<p>Continuous precipitation data are essential for time-series analysis and accurate prediction of hydro-meteorological disasters. However, data gaps introduce significant uncertainty in the assessment of extreme hydro-meteorological events. This study addresses this issue by evaluating statistical methods and gridded precipitation datasets (GPDs) for infilling missing daily rainfall at five target stations in Himachal Pradesh, located in the Himalayan region of India. Statistical approaches utilized observed data from 27 neighboring stations to improve spatial estimation. The accuracy of imputation was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency (NSE), followed by the computation of extreme precipitation indices (EPIs) from the infilled datasets to examine their reliability in representing precipitation extremes. Results indicate that statistical approaches generally outperformed GPDs in reconstructing daily rainfall, with Multiple Linear Regression (MLR) showing the highest overall accuracy across stations. Among gridded datasets, IMD showed the closest agreement with observations, while ERA5-Land and PERSIANN exhibited moderate performance and CHIRPS consistently showed the lowest agreement for daily rainfall reconstruction. MLR also demonstrated relatively better performance in reproducing intensity-based EPIs, while neighbour-based methods performed reasonably for frequency and duration indices. Performance varied with elevation, inter-station distance, and terrain complexity, highlighting the strong influence of orographic processes on precipitation variability in the Himalayas. These findings provide important hydro-meteorological insights and emphasize the need to consider terrain characteristics and station relationships when addressing data gaps and analyzing extreme precipitation in mountainous environments.</p>