Unfortunately, there isn’t such a method for conv layers, as you would have to manipulate the trainable parameters for each different input shape.
Since pooling layers do not have trainable parameters, it’s not problematic to change the kernel shape and setup.
However, the filters in conv layers have a certain shape and if you would like to increase their spatial size, you would have to reinitialize the new elements (or use any other methods such as cloning/mirroring certain values).