Update (Version 2): Added the quality-flag column Q (1–3). Distance values and uncertainties remain unchanged. This dataset provides catalogues of extinction-based distances for Galactic planetary nebulae (PNe), derived using an optimised Gaia-based extinction–distance method, as described in: Deng, Wang & Jiang (2026), “The extinction distances for over one thousand Planetary Nebulae with Gaia measurements”. Background As key tracers of stellar evolution, chemical enrichment, and the interstellar medium, accurate distances to PNe are crucial for determining their intrinsic properties. However, obtaining such distances has long been challenging, as existing methods rarely achieve both broad applicability and high reliability. Despite Gaia's identification of central stars (CSPNe) for ∼70% of known PNe, reliable distances remain scarce: fewer than 25% have accurate parallaxes for deriving distances. To address this limitation, we develop an optimized Gaia-based extinction–distance method for PNe with identified CSPNe, which allows distances to be estimated for over one thousand objects and serves as a complementary approach when parallaxes are uncertain or missing. Data files Distance_for_1066PNe_withQ.csvExtinction-based distance for 1,066 Galactic PNe.Columns: PNG: PN identifier (HASH format) Name: Common PN name D: Distance (pc) D_err: Uncertainty (pc) Q: Quality flag (1–3) indicating the degree of independent external support for the adopted distance Quality flag definition: Q = 3 – Supported by CSPN-based distance estimate(s) and at least one additional independent indicator Q = 2 – Supported either by CSPN-based estimate(s) only or by at least two independent non-CSPN indicators Q = 1 – Supported by a single independent indicator The definition of Q follows Section 5.1 of Deng, Wang & Jiang (2026). CSPN_for_15PNe.csvResults for 15 PNe with disputed CSPN identifications.Columns: PNG: PN identifier Name: PN name source_id: Gaia DR3 source_id of adopted CSPN Usage Data are provided as CSV tables and can be easily accessed in Python, for example with pandas: import pandas as pd pn_catalog = pd.read_csv("Distance_for_1066PNe_withQ.csv") Citation If you use this dataset, please cite: