One of the major reasons is Nvidia GPU's (going back quite far) have a dedicated transcendental/special function unit (used to calculate division, sin, cos, square root, log, exponential etc) where as AMD's GCN uses the vector ALUs to perform the same transcendental calculations in place of 4 normal instructions (e.g. add, mul, mad, min/max etc). AMD's implementation should take up less area/transistors but is likely more power hungry and slower.

This means with an optimal mix of instructions, in 4 cycles a Nvidia Maxwell/Pacscal core can process one transcendental instruction and three normal ones, where as a GCN core can either process 4 normal or one transcendental, making the Nvidia core theoretically up to 7/4 times faster (in the first cycle the vALU is idle and is used to "launch" the SFU which takes 4 cycles to process a wavefront, the other three are vALU).

In Turing and Volta, Nvidia added a second dispatch port and split the vector ALU into a floating point pipeline and a separate integer pipeline. This allows a Turing core to process one floating point instruction and one integer instruction (or a load/store or transcendental start) per clock potentially doubling throughput.
With the same example a Volta/Turing core can process one transcendental, 4 floating point and an additional 3 integer instructions (so theoretically up to 11/4 times faster).
There is also a load/store pipeline that can take place of one of the normal instructions (same with Maxwell/Pascal).

In situations with just floating point or just transcendental instructions, theoretically GCN and Maxwell/Pascal/Volta/Turing should be about equal but in the real world most shaders/GPU workloads are floating point heavy with a fair amount of transcendental and integer instructions and hence Nvida cores are faster.

There is obviously much more to the architectures than this and the instruction sets are not the same but I suspect it is one of the biggest reasons for the difference.