This study addresses the issue of scant availability of sub-daily precipitation data by introducing a novel selection methodology named ‘Pattern Mapping’ (PM) within the existing Method of Fragments (MOF) framework for the disaggregation of daily precipitation. PM introduces multiple statistics based criterion in the selection of fragments thus enhancing accuracy of the selection process. The performance of deterministic and stochastic versions of PM-MOF is compared with three widely used disaggregation methods, namely K Nearest Neighbour based MOF (KNN-MOF), Bartlett-Lewis Rectangular Pulse model (using HYETOS software package) and Micro-Canonical Cascade (MCC) using reanalysis data at 14 global locations spanning various climatic zones. It is seen that the PM-MOF method has considerably lower percentage error in replicating the standard statistics (−50.2 to 50.3% for stochastic PM-MOF and −7.4 to 13.2% for deterministic PM-MOF) when compared to existing methods (KNN-MOF (−65.4 to 76.2%), HYETOS (−78.3 to 69.4%) and MCC (−63.1 to 72%)). The comparison of densities using skill score show that skill score of PM-MOF is 0.97 which is higher than other methods (0.94 for KNN-MOF, 0.83 for HYETOS and 0.85 for MCC). Control experiments designed around varying length of training data and stochastic/deterministic mode (of PM-MOF) ascertain the contribution of the novel methodological improvements in the superior performance of PM-MOF. The robustness of the methodology and geography independent performance of PM-MOF makes it a potent candidate for wider application in hydrological and climate studies.