Achieving precise target jumping with legged robots poses a significant challenge due to the long flight phase and the uncertainties inherent in contact dynamics and hardware. Forcefully attempting these agile motions on hardware could result in severe failures and potential damage. Motivated by this challenge, we propose an Iterative Learning Control (ILC) approach to learn and refine jumping skills from easy to difficult, instead of directly learning these challenging tasks. We verify that learning from simplicity can enhance safety and target jumping accuracy over trials. Compared to other ILC approaches for legged locomotion, our method can tackle the problem of a long flight phase where control input is not available. In addition, our approach allows the robot to apply what it learns from a simple jumping task to accomplish more challenging tasks within a few trials directly in hardware, instead of learning from scratch. We validate the method through extensive experiments on the Al model and hardware for various tasks. Starting from a small jump (e.g., a forward jump 40cm), our learning approach empowers the robot to accomplish a variety of challenging targets, including jumping onto a 20cm high box, leaping to a greater distance of up to 60cm, as well as performing jumps while carrying an unknown payload of 2kg. Our framework allows the robot to reach the desired position and orientation targets with approximate errors of 1 cm and 10 within a few trials.