Many conventional daylighting design tools are limited in that each simulation represents only one time of year and time of day (or a single, theoretical overcast sky condition). Since daylight is so variable - due to the movement of the sun, changing seasons, and diverse weather conditions - one moment is hardly representative of the overall quality of the daylighting design, which is why climate-based, dynamic performance metrics like Daylight Autonomy (DA) and Useful Daylight Illuminance (UDI) are so needed. Going one step further, the annual variation in performance (condensed to a percentage by DA and UDI) is also valuable information, as is the ability to link this data to spatial visualizations and renderings. Trying to realize this combination of analytical needs using existing tools would become an overly time-consuming and tedious process. The challenge is to provide all information necessary to early design stage decision-making in a manageable form, while retaining the continuity of annual data. This paper introduces a climate data simplification method based on a splitting of the year into 56 periods, over which weather conditions are "averaged" and simulated using Perez's ASRC-CIE sky model, while information on sun penetration is provided at a greater resolution. The graphical output of the produced data in the form of "Temporal Maps" will be shown to be visually, and even numerically, comparable to reference case maps created using short time step calculations and based on illuminance data generated by Daysim.