This study demonstrates the capability of the Weather Research and Forecasting (WRF) model with four-dimensional data assimilation (WRF-FDDA) to produce a high-resolution climatography of seasonal precipitation over Israel and the surrounding areas. The system was used to dynamically downscale global Climate Forecast System (CFS) reanalysis with continuous assimilation of conventional and unconventional observations. Precipitation seasons (December-January-February) in 7 years, including two extreme dry and wet seasons observed in the past decades, were generated at 2-km spatial resolution. Verification against rain-gauge observations shows that the WRF-FDDA system effectively reproduces the spatial and inter-annual variability, as well as the timing, intensity, and length of wet and dry spells. The best agreement between model and observations was obtained at areas dominated by complex terrain, illustrating the benefit of the high-resolution lower boundary forcing in the dynamical downscaling process. In contrast, some biases were observed over coastal-flat terrain. The model was able to reproduce some of the extreme events, but exhibited limitations in the case of rare events. This specific discrepancy between the model and observations suggests that further fine tuning and different model configurations may be needed to correctly simulate extreme events. The use of an objective weather-regimes verification procedure reveals the skill of the climatography for different types of extra-tropical cyclones: while biases are larger at coastal-flat areas under shallow-cyclonic conditions, deep-cyclonic conditions lead to more significant biases in complex terrain regions. The weather-regimes dependent information may be used for further calibration of the downscaled precipitation.