One of the biggest challenges in developing a numerical weather prediction model is incorporating the effects of sub-grid-scale processes. Sub-grid-scale processes operate on scales that are smaller than can be resolved directly by the model. For example, a numerical weather prediction model run with a 25-km grid spacing cannot possibly resolve boundary layer turbulence and eddies, which operate at scales of 1 km or less.
Numerical modelers develop parameterizations to try and account for the effects of sub-grid-scale processes in their models. One sub-grid-scale process that is particularly problematic to deal with is cumulus convection, which may be deep (i.e., precipitating) or shallow (i.e., non-precipitation). Given the incredible variety of environments and scales in which cumulus convection operates, this is a very difficult problem, one that I have often called the "black hole of meteorology."
Case in point is the latest (0600 UTC) GFS model run. In particular, note how spotty the precipitation is at the two times below and how the precipitation tends to fall at the same location both times.
Such a solution is what I like to call unphysical. Although there is a tendency in the real world for convection to initiate and precipitation to fall over higher terrain, the GFS always produces precipitation at the same locations when the synoptic forcing is weak. I'll call these grid-point storms, even if they don't meet the definition perfectly.
The basic problem is that neither monsoon convection, nor the topography that contributes to its initiation and evolution, can be resolved by the GFS. During the day, the GFS preferentially generates precipitation in areas of thermally forced convergence, which is controlled by the highly smoothed GFS topography. The GFS is also unable to generate realistic cold pools and outflow boundaries that contribute to secondary convective initiation, which may occur over valleys and basins. The bottom line is that we end up with precipitation tending to fall at the same locations each day.
Unfortunately, this means that the GFS has very little skill at predicting the mountain vs. lowland distribution of precipitation on days with monsoon convection. Perhaps it is unreasonable to expect much skill given the chaotic nature of convection, but it will be important to ensure that such model biases are addressed as we move to higher resolution model ensembles for weather forecasting.