Process mining extracts models from event logs to provide insights into workflows. Current techniques often struggle to capture counting-based dependencies between activities. We address this challenge by introducing guarded process trees, an enhancement of traditional process trees with guards that control transitions based on activity occurrence counts. We present a two-step method for translating guarded process trees to Petri nets: first, converting to a guarded Petri net, then removing the guards while preserving the counting semantics. We formally define and prove the correctness of both translation steps. To demonstrate practical applicability, we develop a method for learning guarded process trees from event logs and implement it using grammatical evolution. Through experiments on both synthetic and real-world datasets, we show that our approach significantly improves precision compared to existing techniques while maintaining model interpretability. The evaluation demonstrates average precision improvements of $15.31 \%$ across datasets when enhancing the Inductive Miner with guards, with individual improvements ranging from $2.06 \%$ to $70.51 \%$.