Adjusting for tilt of the earth I have rewritten the old question (below) to hopefully make things a bit clearer.
Basically I think that the temperature of the earth should be normally distributed but is not due to the ‘seasonal tilt’ and curvature of the earth’s surface. I have added some histograms for different ‘latitude bands’ of the temperatures and rainfall in mm here (I have included rainfall here but please ignore it for now). For example, [-10 ... 10] corresponds to latitude band (-)10 degrees south to 10 degrees north inclusively. 
Of course temperature depends on latitude but this is due to the fact that it actually depends on seasonal tilt and curvature IMHO. 
At the end of the day I am after some transformation which removes the effect of seasonal tilt/curvature to arrive at some global normal distribution for all four seasons. Does this make sense?
Thanks.
Christian
Old question:
I have some data of the earth's surface (temperature + rainfall in mm):

Clearly the earth's tilt affects the modality/normality of, for example, the temperature distributions. I am just wondering whether there is a way to adjust for this to make the (combined?) data more normal/less modal?
I am not a statistician ... so not sure whether this is possible? Thanks in advance.
Christian
 A: Unfortunately, temperature depends upon much more than latitude; elevation counts for a whole lot, as well as forestation and nearby bodies of water and local geography.  Similarly with rainfall, which depends enormously upon local geography, for example, the Hoh rainforest in Olympic National Park gets ~150 inches of rain a year, but 30 miles due east gets ~15 inches of rain a year.
Having said that, if you have a collection of particular sites, you could try fitting a cosine with period 1 year to a multiyear series of daily mean temperatures - different fits for each site.  I recall I did something like this some years ago, and it worked pretty well.  IIRC, the minimum temperature date (the point at which the argument to the cos function should equal $\pi$) is Jan 7th over pretty much the entire U.S., including Alaska, but you should estimate this too.  Something like this:
$Temp_i = a + b*cos(c + 2*\pi * i/365.25)+e_i$
where $i = 1$ on Jan. 1 and 365 (or 366) on Dec. 31.  
The $a$ and $b$ coefficients will vary with location.  $a$ will be the mean annual temperature at the location, and $b$ will equal, more or less, 1/2 the difference between the average max and average min temperatures.
Something like this may provide you with a model that will allow you to remove the systematic component due to earth tilt and the varying distance of the earth from the sun (minor) of temperature at a given site.  Of course, the errors will be autocorrelated due to persistence of local weather patterns, e.g., high pressure systems.  Also of course, it doesn't really help with your stated goal, but may get you part way towards your underlying objective.  
A: I doubt there is much that will help with the idiosyncratic, jagged shape of temperature, but with rainfall there are data-transformations you can try.  You can search for that tag on this site and you'll come up with many useful posts.  (Note that "normalization" is defined differently from "data-transformation" and it is the latter that you want in order to create something closer to a bell curve.)  
The first thing I would try would be to take the square root of each value.  The result may still be skewed, but probably a lot less so.  In other words, it would probably not "pass" a normality test (those are pretty unreliable anyway), but it would likely be a more workable variable for most purposes.  As many on this site have reminded us, in multivariate analysis it is typically the residuals, rather than the univariate distributions, that must be close to normal in order to satisfy the normality assumption.
