Disclaimer: I am not a meteorologist, but a friend of mine is and I had discussed this with him some time ago. Paraphrasing from memory, I might make mistakes.

Only in recent decade or two we got enough computational power to run huge weather models (such as Aladin) comfortably, with live updating on incoming data. However, the model is only as good as the input data - if the meteorological weather stations are positioned every few kilometers, the prediction is extremely good, but if they are more sparse, the errors increase. The complexity of terrain plays a role too - on a flat plain, weather stations might reasonably be spaced tens of km's, but a small hill means they have to be spaced much more closely to get reasonable results.

There is also sometimes very helpful "local knowledge" - e.g. whatever the abrupt weather change in Germany, you can be reasonably sure in 3 days it will happen in Slovakia.

Taking this into account, professional weather forecast is very reliable for a day or two - enough to leave your umbrella at home. However, interpretation by news forecasters glosses over finer details, such as "sunny, high temperature with 10% chance of rain" will be interpreted as "sunny, high temperature, a little of rain" and give false signals.