This paper proposes a new game-search algorithm, PN-MCTS, which combinesMonte-Carlo Tree Search (MCTS) and Proof-Number Search (PNS). These twoalgorithms have been successfully applied for decision making in a range ofdomains. We define three areas where the additional knowledge provided by theproof and disproof numbers gathered in MCTS trees might be used: final moveselection, solving subtrees, and the UCB1 selection mechanism. We test allpossible combinations on different time settings, playing against vanilla UCTon several games: Lines of Action ($7$$\times$$7$ and $8$$\times$$8$ boardsizes), MiniShogi, Knightthrough, and Awari. Furthermore, we extend this newalgorithm to properly address games with draws, like Awari, by adding anadditional layer of PNS on top of the MCTS tree. The experiments show thatPN-MCTS is able to outperform MCTS in all tested game domains, achieving winrates up to 96.2% for Lines of Action.