The well recognized constraint of non-linear and non-Gaussian distribution of rainfall observation limits its assimilation in the high-dimensional numerical weather prediction (NWP) model. In this study, rain-gauges’ observed rainfall from Indian Meteorological Department (IMD) over Indian landmass is assimilated in the Weather Research and Forecasting (WRF) model using particle filter. In the framework of imperfect weather model, particles (or ensembles) for rainfall predictions are created with various combinations of model physics (viz. cumulus parameterization, micro-physics, planetary boundary layer schemes). With the help of IMD observed rainfall, weights are provided to various particles using multiple hypotheses, and this is the step in which IMD rainfall observations are used for assimilation. Further, a resampling step is performed to generate new particle from high weight particle using stochastic kinetic-energy backscatter scheme (SKEBS) method in which dynamical variables are perturbed into the model physics. Results based on rainfall verification scores suggest that assimilation of the rain-gauges observed rainfall using particle filter improved prediction of rainfall over CNT runs (unweighted particle; without assimilation). Moreover, surface and vertical profile of temperature, water vapour mixing ratio (WVMR) and wind speed are also improved in 24 h forecasts.