Title: The 3TConv: An Intrinsic Approach to Explainable 3D CNNs. Abstract: Current deep learning architectures that make use of the 3D convolution (3DConv) achieve state-of-the-art results on action recognition benchmarks. However, the 3DConv does not easily lend itself to explainable model decisions. To this end we introduce a novel and intrinsic approach, whereby all the aspects of the 3DConv are rendered explainable. Our approach proposes the temporally factorized 3D convolution (3TConv) as an interpretable alternative to the regular 3DConv. In a 3TConv the 3D convolutional filter is obtained by learning a 2D filter and a set of temporal transformation parameters, resulting in a sparse filter requiring less parameters. We demonstrate that 3TConv learns temporal transformations that afford a direct interpretation by analyzing the transformation parameter statistics on a model level. Our experiments show that in the low-data regime the 3TConv outperforms 3DConv and R(2+1)D while containing up to 77\% less parameters.