Weather generators based on resampling simulate new time series of weather variables by reordering the observed values such that the statistics of the simulated data are coherent with the observed ones. These weather generators are fully data-driven and simple to implement, do not rely on parametric distributions, and can reproduce the dynamics among the weather variables under analysis. However, although the simulated time series is new, the produced weather fields at arbitrary timesteps are copies of the weather fields found in the training dataset. Consequently, the spatial variability of simulations is restricted. Furthermore, these weather generators cannot create weather fields with out-of-sample extreme values because the scope of the resampling method is constrained to the observed values. In this work, we embedd the Direct Sampling algorithm — a data-driven method for producing simulations — into resampling-based weather generators to improve the spatial variability of the produced weather fields, and for generating extreme weather fields. We increase the spatial variability by applying Direct Sampling as a post-processing step on the weather generator outputs. Furthermore, we produce out-of-sample extreme weather fields using Direct Sampling in two ways: 1) applying quantile mappings on the Direct Sampling simulations for a given return period, and 2) using a set of control points jointly with Direct Sampling with values informed by return period analysis. We validate our approach using precipitation, temperature, and cloud cover weather-fields time series datasets, for a region in northwest India. The results are analyzed using a set of statistical and connectivity metrics.