I'll be interested in practicalities such as how propmtly the data will appear in each month. GHCN V3 was prone to aberrations in reporting; that may get worse. We'll see. Anyway, I've run it through TempLS grid. The initial run of the mesh version will take several hours, so I'm waiting to get some decisions right. It's possible I should opt for a subset of stations, and I'll probably want to modify the policy on SST stations. Currently I reduce from a 2°x2° grid to a 4°x4°, mainly because otherwise SST would be over-treated relative to land. This is partly to reduce effort, but also to reduce the tendency for SST values to drift into undercovered land regions. Now the original SST grid is comparable to land, so the case for saving effort is less. The encroachment issue may remain.
The other issue is whether the land data should be pruned in some way. I think it probably should.
Anyway, I'll show below the WebGL plot, with land stations marked, for September 2015, done by TempLS grid. I think it is the best way to see the distribution. You can zoom with the right button (N-S motion), and Shift-Click to show details of the nearest station. The gadget is similar to the maintained GHCN V3 monthly page, which you can use for comparison. You can move the earth by dragging, or by clicking on the top right map. Incidentally, the gadget download is now about 4Mb, and may take a few seconds. I'll work on this.
The plot shows the shaded anomalies for September. Incidentally, the average was 0.788°C, vs 0.761°C for V3. Generally, the differences in TempLS grid are very small. August was almost identical. You can see that some regions have very dense cover, for example, Germany, Japan, Australia and the US. Regions that were sparsely covered in V3, such as the swathe through Nabibia, Zaire etc, are still sparse. I don't see much improvement in Antarctic, and Arctic has some extra, but still not good coverage.
Here is a list of the top 20 countries reporting in September. You can see the excessive numbers in the US, Australia and Canada, and proportionately, in Germany and Japan. Almost half are in the US. I think I will have to thin these out. The inventory, however, is now just bare data of lat, Lon, Alt and name. So it isn't easy to pick out rurals, for example.