The increased frequency of meteorological disasters has been observed due to increased extreme events such as heavy rainfalls and
flash floods. Numerous studies using high-resolution weather radar rainfall data have been carried out on the hydrological effects. In this
study, a conditional merging technique is employed, which makes use of geostatistical methods to extract the optimal information from
the observed data. In this context, three different techniques such as kriging, inverse distance weighting and spline interpolation methods
are applied to conditionally merge radar and ground rainfall data. The results show that the estimated rainfall not only reproduce the
spatial pattern of sub-hourly rainfall with a relatively small error, but also provide reliable temporal estimates of radar rainfall. The
proposed modeling framework provides feasibility of using conditionally merged rainfall estimation at high spatio-temporal resolution
in ungauged areas.