Accurate detection of tropical cyclones (TCs) from gridded climate model data is essential for evaluating model performance and projecting future TC activity. Conventional detection schemes rely on environmental variable thresholds that are frequently tuned to specific basins or models, making them inherently subjective. Conversely, detection schemes based on universal thresholds often fail to capture regional characteristics. While recent machine learning (ML) approaches provide objective data-driven thresholds, they generally require a great number of variables compared to conventional methods and suffer from high false alarm rates (FAR). Here, we introduce an objective hybrid tracker and classifier for tropical cyclones (TC²), an algorithm combining traditional and ML techniques to establish objective thresholds and minimize FAR. Using an ensemble of six classifiers based on three ML algorithms and two reanalyses, TC² avoids dependency on specific ML algorithms or datasets. TC² was trained and its hyperparameters were optimized using two reanalysis datasets over 1998–2017 period, while its performance was evaluated on their respective internal test sets and the independent NCEP FNL dataset from 2018 to 2024. Evaluated against the NCEP FNL dataset, TC² outperforms the existing algorithms, i.e., TempestExtremes and OWZP, achieving higher F1 score (83.6 %) and critical success index (71.8 %), while significantly lowering FAR (14.3 %) and maintaining a comparable hit rate (81.5 %). TC² also better reproduces TC count and seasonal variability over each basin. In CMIP6 evaluations, TC² successfully captures the overall characteristics of TC activity. Under the SSP2-4.5 scenario, projected spatial changes in TC genesis frequency detected by TC² are largely consistent with those of the dynamical genesis potential index, suggesting that TC² identifies physically coherent systems governed by large-scale dynamic environments. Utilizing only a limited set of commonly available variables, including minimum sea level pressure, low-level relative vorticity, upper- and low-level wind speeds, and an upper-level warm core, TC² provides an effective and robust framework for TC detection in gridded climate datasets.